Device for determining a condition of an organ and method of operating the same

ABSTRACT

In various embodiments, device for determining a condition of an organ of either a human or an animal may be provided. The device may include a first optical source and a second optical source. The device may also include a detector. The device may additionally include a lens system. The device may further include a switching mechanism configured to switch between an optical examination mode and a Raman mode. The lens system during the optical examination mode may be configured to direct a first light emitted from the first optical source. The lens system during the Raman mode may be configured to direct a second light emitted from the second optical source. The lens systems during the Raman mode may be further configured to direct a third light to the detector.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority of U.S. patentapplication Ser. No. 61/926,518, filed 13 Jan. 2014, the content of itbeing hereby incorporated by reference in its entirety for all purposes.

TECHNICAL FIELD

Various aspects of this disclosure relate to devices for determining acondition of an organ of either a human or an animal as well as methodsof operating the same.

BACKGROUND

Eye infection is a serious clinical problem, which has a high likelihoodto lead to blindness without proper treatment. According to the Bulletinof the World Health Organization, eye infection lead to about 1 millionnew cases of blindness in Asia alone. One major cause of this seriousdisease is the infection caused by microorganisms such as bacteria andfungi, which usually occurs after intraocular surgery or simply isinduced by a distant infective source in the body. Early diagnosis iscritical in the management of this disease. The essential prerequisitefor the optimal treatment of eye infection is to identify themicroorganism causing infection as each type of microorganism causingeye infection requires a different therapeutic approach. For example,systemic antimicrobial therapy is usually recommended for patients withendogenous endophthalmitis. In this case, the type and extent of theinfection needs to be diagnosed to determine potential complications andfind underlying systemic cause or risk factors.

The current clinical procedure for identifying the microorganism speciescausing eye infection includes Gram staining and culture of aqueous andvitreous smear samples taken from the surface of infected eyes, which istypically performed in the pathology or microbiology department. Gramstaining empirically differentiates bacterial species into two largegroups (Gram-positive and Gram-negative) based on the chemical andphysical properties of their cell walls. It is fast and cheap. However,it is not meant to be a definitive tool for diagnosis. For example, itonly works for bacteria and not every bacterium can be definitivelyclassified. Culture is considered as the gold standard but thisprocedure is labor intensive and expensive. The cost for Gram stainingand culture can range from about 60 Singapore dollars to about 180Singapore dollars for material charge alone (excluding labor), notmentioning a much larger cost incurred for disease management if nottreated in time and appropriately. It usually takes a few days toculture the microorganisms in smears to get reliable results. Such along delay in diagnosis could result in the exacerbation of patients'symptoms. The delay may also lead to the optimal time frame fortreatment being missed as well as the subsequent rising cost for diseasemanagement. In addition, taking smear samples from eyes for culturing isunpleasant and can be challenging in some patients. In addition to Gramstaining and culturing, Polymerase Chain Reaction (PCR) is sometimesused to assist diagnosis especially for those species that cannot becultured but PCR is in general expensive and its false-positive rate isoften high.

SUMMARY

In various embodiments, device for determining a condition of an organof either a human or an animal may be provided. The device may include afirst optical source and a second optical source. The device may alsoinclude a detector. The device may additionally include a lens system.The device may further include a switching mechanism configured toswitch between an optical examination mode and a Raman mode. The lenssystem during the optical examination mode may be configured to direct afirst light emitted from the first optical source. The lens systemduring the Raman mode may be configured to direct a second light emittedfrom the second optical source. The lens systems during the Raman modemay be further configured to direct a third light to the variousembodiments, a method of operating a device for determining a conditionof an organ of either a human or an animal. The method may includeactivating a switching mechanism to switch between an opticalexamination mode and a Raman mode. During the optical examination mode,a lens system may be configured to direct a first light emitted from afirst optical source. During the Raman mode, the lens system may beconfigured to direct a second light emitted from a second opticalsource. During the Raman mode, the lens systems may be furtherconfigured to direct a third light to the detector.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be better understood with reference to the detaileddescription when considered in conjunction with the non-limitingexamples and the accompanying drawings, in which:

FIG. 1 is a schematic illustrating a device for detecting a condition ofan organ of either a human or an animal according to variousembodiments.

FIG. 2 is a schematic illustrating a slit lamp system according tovarious embodiments.

FIG. 3 is a schematic illustrating a device according to variousembodiments.

FIG. 4A is a schematic 400 a of a setup to illustrate focusing accordingto various embodiments.

FIG. 4B is an image of the setup illustrated in FIG. 4A according tovarious embodiments.

FIG. 5A is a sequence of images obtained at the detector according tovarious embodiments.

FIG. 5B is a plot of the focus index as a function of distance inmicrons.

FIG. 6 is a schematic of a setup to illustrate auto-focusing accordingto various embodiments.

FIG. 7 is a schematic of a setup to illustrate line auto-focusingaccording to various embodiments.

FIG. 8A is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating the best case of the test phantoms.

FIG. 8B is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating a typical case of the test phantoms.

FIG. 8C is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating the worst case of the test phantoms.

FIG. 9 is a schematic illustrating a procedure for Wiener estimationaccording to various embodiments.

FIG. 10A is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating an original spontaneous Raman data, includingfluorescence background, of leukemia cells.

FIG. 10B is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating an original surface enhanced Raman spectroscopy(SERS) data, including fluorescence background, of blood serum sample.

FIG. 11A is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating measured spontaneous Raman spectrum and thespontaneous Raman spectrum reconstructed by traditional Wienerestimation for the best case using the best combination of sixcommercial filters.

FIG. 11B is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating measured spontaneous Raman spectrum and thespontaneous Raman spectrum reconstructed by traditional Wienerestimation for a typical case using the best combination of sixcommercial filters.

FIG. 11C is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating measured spontaneous Raman spectrum and thespontaneous Raman spectrum reconstructed by traditional Wienerestimation for the worst case using the best combination of sixcommercial filters.

FIG. 11D is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating the transmittance spectra of the six commercialfilters corresponding to the typical case.

FIG. 12A is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating measured spontaneous Raman spectrum and thespontaneous Raman spectrum reconstructed by traditional Wienerestimation for the best case using the best combination of sixnon-negative principal components (PCs) based filters.

FIG. 12B is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating measured spontaneous Raman spectrum and thespontaneous Raman spectrum reconstructed by traditional Wienerestimation for a typical case using the best combination of sixnon-negative principal components (PCs) based filters.

FIG. 12C is a 1200 c of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating measured spontaneous Raman spectrum and thespontaneous Raman spectrum reconstructed by traditional Wienerestimation for the worst case using the best combination of sixnon-negative principal components (PCs) based filters.

FIG. 12D is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating the transmittance spectra of the six non-negativeprincipal components (PCs) based filters corresponding to the typicalcase.

FIG. 13A is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating measured surface enhanced Raman spectroscopy (SERS)spectrum and the surface enhanced Raman spectroscopy (SERS) Ramanspectrum reconstructed by traditional Wiener estimation for the bestcase using the best combination of six commercial filters.

FIG. 13B is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating measured surface enhanced Raman spectroscopy (SERS)spectrum and the surface enhanced Raman spectroscopy (SERS) spectrumreconstructed by traditional Wiener estimation for a typical case usingthe best combination of six commercial filters.

FIG. 13C is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating measured surface enhanced Raman spectroscopy (SERS)spectrum and the surface enhanced Raman spectroscopy (SERS) spectrumreconstructed by traditional Wiener estimation for the worst case usingthe best combination of six commercial filters.

FIG. 13D is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating the transmittance spectra of the six commercialfilters corresponding to the typical case.

FIG. 14A is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating measured surface enhanced Raman spectroscopy (SERS)spectrum and the surface enhanced Raman spectroscopy (SERS) spectrumreconstructed by traditional Wiener estimation for the best case usingthe best combination of six non-negative principal components (PCs)based filters.

FIG. 14B is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating measured surface enhanced Raman spectroscopy (SERS)spectrum and the surface enhanced Raman spectroscopy (SERS) spectrumreconstructed by traditional Wiener estimation for a typical case usingthe best combination of six non-negative principal components (PCs)based filters.

FIG. 14C is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating measured surface enhanced Raman spectroscopy (SERS)spectrum and the surface enhanced Raman spectroscopy (SERS) spectrumreconstructed by traditional Wiener estimation for the worst case usingthe best combination of six non-negative principal components (PCs)based filters.

FIG. 14D is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating the transmittance spectra of the six non-negativeprincipal components (PCs) based filters corresponding to the typicalcase.

FIG. 15 is a schematic illustrating a method of operating a device fordetermining a condition of an organ of either a human or an animalaccording to various embodiments.

FIG. 16 is a schematic of a device for determining a condition of anorgan of either a human or an animal according to various embodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawingsthat show, by way of illustration, specific details and embodiments inwhich the invention may be practiced.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration”. Any embodiment or design described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs.

It should be understood that the terms “on”, “over”, “top”, “bottom”,“down”, “side”, “back”, “left”, “right”, “front”, “lateral”, “side”,“up”, “down” etc., when used in the following description are used forconvenience and to aid understanding of relative positions ordirections, and not intended to limit the orientation of any device, orstructure or any part of any device or structure.

The time consuming and costly culture procedure currently used inclinical practice may warrant the development of a new clinical method,which may be able to rapidly and accurately identify the microorganismcausing eye infection. Such a method may assist an ophthalmologist tomake appropriate therapeutic strategies in a timely manner. Furthermore,it may be desirable to eliminate the unpleasant step of getting smearsamples from the eye for the benefit of patients thus the method to bedeveloped should be able to scan the eye non-invasively. A device foruse the such a clinical method may be developed.

FIG. 1 is a schematic 100 illustrating a device for detecting acondition of an organ of either a human or an animal according tovarious embodiments. The device may include a first optical source 102and a second optical source 104. The device may also include a detector106. The device may additionally include a lens system 108. The devicemay further include a switching mechanism 110 configured to switchbetween an optical examination mode and a Raman mode. The lens system108 during the optical examination mode may be configured to direct afirst light emitted from the first optical source 102. The lens system108 during the Raman mode may be configured to direct a second lightemitted from the second optical source 104. The lens systems 108 duringthe Raman mode may be further configured to direct a third light to thedetector 106.

In other words, the device may be configured to operate in two modes: anoptical examination mode and a Raman mode. The device may be configuredto emit a first light from a first optical source 102 during the opticalexamination mode. The device may be configured to emit a second lightfrom a second optical source 104 and detect a third light using thedetector 106 during the Raman mode. The two modes may be switchedbetween each other using a switching mechanism 110.

The organ may be an eye. The device may be or may include anopthalmoscope with a Raman module.

In various embodiments, the first optical source 102 may be or mayinclude an incoherent light source. The second optical source 104 may beor may include a laser module.

The third light may be derived or may be based on the second light. Thesecond light emitted by second optical source 104 may incident on theorgan, e.g. an eye. The light reflected by the organ may be the thirdlight. Similarly, a first light emitted by the first optical source 102may incident on the organ to generate or derive a fourth light. Thefourth light may be the light reflected by the organ when the firstlight is incident on the organ.

The second light and the third light may be laser. The second lightemitted from the second optical source 104 may have a frequency shiftfrom the third light directed to the detector 106. In other words, thesecond light may have a first frequency and the third light may have asecond frequency. The first frequency and the second frequency may bedifferent.

The first light and the fourth light may be incoherent light. Theelectromagnetic waves making up the first light may not have a constantphase difference and constant frequency. Similarly, the electromagneticwaves making up the fourth light may not have a constant phasedifference and constant frequency. In various alternate embodiments, thefirst light and the fourth light may instead be coherent light or alaser and the first optical source may be a coherent light source or alaser source, such as in a laser opthalmoscope.

The device may further include an interface portion. The interfaceportion may be a portion of the slit lamp system in which the firstlight exits from the device and in which the fourth light enters thedevice. During the optical examination mode, the lens system 108 may beconfigured to direct the first light emitted from the first opticalsource 102 to the interface portion. The first light may be transmittedthrough the interface portion towards the organ. The fourth lightreflected from the organ may be transmitted again through the interfaceportion. The fourth light may be in an opposing direction to the firstlight. The device or the lens system 108 may be configured to direct thefourth light to an obsever such as an opthalmologist or a doctor. Theobserver may thus be able to examine the organ. The device may includean optical examination output portion such as an eye piece. The deviceor the lens system 108 during the optical examination mode may befurther configured to direct the fourth light from the interface portionto the optical examination output portion. The fourth light may bederived from the first light. The fourth light may be reflected by theorgan when the first light is incident on the organ. The fourth lightmay pass through the optical examination output portion to the observer.The observer may examine the organ by looking through or at the opticalexamination output portion.

During the Raman examination mode, the lens system during the Raman modemay be configured to direct the second light emitted from the secondoptical source to the interface portion. The second light may betransmitted through the interface portion towards the organ. The thirdlight reflected from the organ may be transmitted again through theinterface portion. The third light may be in an opposing direction tothe light. The device or the lens system 108 during the Raman mode maybe configured to direct the third light to the detector.

The lens system 108 may include an objective lens for focusing thesecond light (onto the organ such as the eye). The lens system 108 mayfurther include an actuator for controlling a position of the objectivelens. By controlling the objective lens, the actuator may control thefocusing of the second light onto the organ. The actuator is apiezoelectric transducer. The actuator may move the objective lens uponapplication of a voltage to the actuator. The lens system 108 mayfurther include an actuator feedback circuit coupling the detector tothe actuator.

In various embodiments, a “circuit” may be understood as any kind of alogic implementing entity, which may be special purpose circuitry or aprocessor executing software stored in a memory, firmware, or anycombination thereof. Thus, in various embodiments, a “circuit” may be ahard-wired logic circuit or a programmable logic circuit such as aprogrammable processor, e.g. a microprocessor (e.g. a ComplexInstruction Set Computer (CISC) processor or a Reduced Instruction SetComputer (RISC) processor). A “circuit” may also be a processorexecuting software, e.g. any kind of computer program, e.g. a computerprogram using a virtual machine code such as e.g. Java.

The actuator feedback circuit may be configured to receive an outputfrom the detector and further configured to provide a feedback to theactuator based on the output from the detector 106. The actuatorfeedback circuit may allow for autofocusing. The actuator feedbackcircuit may be configured to determine a focus index based on the fromthe detector 106. The actuator feedback circuit may be configured todetermine a plurality of focus indexes based on a plurality of outputsfrom the detector when the objective lens is moved during calibration.The actuator feedback circuit may be further configured to determine amaximum focus index based on the plurality of focus indexes. Theactuator may be configured to move the objective lens during operationuntil the index is at a reference focus index value, i.e. at thecalibrated maximum focus index. The focusing of the first light onto theorgan may be optimal when the focus index is at the predetermined value.

In other words, the actuator feedback circuit may be configured todetermine a focus index based on the output from the detector and may befurther configured to provide a feedback based on the determined focusindex and a reference focus index. The actuator feedback circuit may befurther configured to control the actuator to move the objective lensuntil the determined focus index is substantially equal to the referencefocus index.

The lens system may include a spatial light modulator (SLM) formodulating the second light emitted from the second optical sourcereflected (to the organ). In other words, the spatial light modulator(SLM) is configured to reflect the second light from the second opticalsource to the organ. In various alternate embodiments, the lens systemmay include another dynamic optical element, such as a digitalmicromirror device, for modulating the second light emitted from theoptical source reflected. The spatial light modulator or dynamic opticalelement may be configured to reflect different intensities of light. Thelens system may further include a spatial light modulator feedbackcircuit (or a dynamic optical element feedback circuit) coupling thedetector to the spatial light modulator (or dynamic optical element). Invarious embodiments, the spatial light modulator feedback circuit (or adynamic optical element feedback circuit) may be configured, e.g. duringoperation, to generate a skeletonized line based on a line formed by thesecond light (on the organ). The spatial light modulator (or dynamicoptical element) may be configured to be adjusted based on a feedback,e.g. a feedback voltage, from the spatial light modulator feedbackcircuit (or a dynamic optical element feedback circuit) until a focusindex of each pixel along a subsequent skeletonized line generatedreaches a maximum value. The spatial light modulator (or dynamic opticalelement) may be configured to reflect different intensities of lightbased on the feedback. In other words, the lens system may include adynamic optical element for modulating the second light emitted from thesecond optical source reflected (to the organ). The dynamic opticalelement may be a spatial light modulator (SLM) or a digital micromirrordevice. The lens system may further include a dynamic optical elementfeedback circuit coupling the detector to the dynamic optical element.The dynamic optical element feedback circuit may be configured togenerate a skeletonized line based on a line formed by the second light

The maximum value may be predetermined, e.g. during a calibration stage.During a calibration stage, the spacial light modulator (or dynamicoptical element) may be adjusted and a plurality of focus indexes ofeach pixel may be determined. The maximum value may be determined basedon the plurality of focus indexes of each pixel. During operation, thespatial light modulator (or dynamic optical element) may be adjusteduntil the maximum value is reached.

The lens system 108 may include one or more beam splitters or dichroicmirrors configured to direct the first light during the opticalexamination mode and further configured to direct the second lightduring the Raman mode. In other words, the one or more beam splitters ordichroic mirrors may be used in both modes, i.e. shared for both modes.

The device may further include a processor coupled to the detector 106.The or lens system 108 may additionally include one or more filtersconfigured to generate one or more narrow-band Raman images from animage (of the organ) captured by the detector 106. The processor may beconfigured to generate one or more reconstructed Raman images based onthe one or more narrow Raman images. Each of the one or morereconstructed images may correspond to one wavelength or one range ofwavelengths. The processor may be further configured to generate a Ramanspectrum at each pixel based on the one or more reconstructed Ramanimages. There may be a plurality, e.g. hundreds, of reconstructedimages, each corresponding to one wavelength (or wavenumber). Theintensity values of these reconstructed images may be concatenated toform a Raman spectrum at each pixel.

The one or more filters may be configured to generate one or morereference narrow-band Raman images from one or more reference images,for instance, during the calibration stage. The one or more referenceimages may contain or include full spectral information at each pixel(for all pixels). The processor may be further configured to determine aWiener matrix based on the one or more reference narrow-band Ramanimages and the one or more reference images. The one or more referenceimages may be generated based on the one or more reference samples. Eachreference sample may include one or more basic (biochemical) components.The processor may be configured to generate the one or morereconstructed Raman images based on the one or more narrow-band Ramanimages and the Wiener matrix. The processor may be configured to removefluorescence background from the one or more reconstructed Raman images.The one or more narrow-band Raman images may have a spectral resolutionlower than the one or more reconstructed Raman images. The one or morefilters may include one or more principal component filters.Additionally or alternatively, the one or more filters may include oneor more commercial filters and/or one or more gaussian filters. The oneor more filters may be generated from one or more principal componentsbased on or calculated from Raman spectra of the reference portion.

FIG. 2 is a schematic 200 illustrating a slit lamp system according tovarious embodiments. The slit lamp may be configured to determine acondition of an organ such as an eye 214. The slit lamp system mayinclude an optical source 202 and a lens system 208. The lens system maybe configured to direct a first light (indicated by 218 a) emitted fromthe first optical source 202 to the eye 214. A fourth light (indicatedby 218 b) may be reflected from the eye 214.

The slit lamp system may further include an interface portion 210. Theinterface portion 210 may provide an interface between the slit lampsystem and the eye 214. The interface portion 210 may be a portion ofthe slit lamp system in which the first light exits from the slit lampsystem and in which the fourth light enters the slit lamp system. Thelens system 208 may be configured to direct the first light emitted fromthe first optical source 202 to the interface portion 210. The firstlight may be transmitted through the interface portion 210 towards theeye 214. The fourth light reflected from the organ may be transmittedagain through the interface portion 210. The fourth light may be in anopposing direction to the first light. The lens system 208 may beconfigured to direct the fourth light to an obsever such as anopthalmologist or a doctor. The observer may thus be able to examine theorgan. The slit lamp system may include an optical examination outputportion 212 such as an eye piece. The lens system 208 may be furtherconfigured to direct the fourth light, the fourth light derived from thefirst light, from the interface portion 210 to the optical examinationoutput portion 212. The fourth light may pass through the opticalexamination output portion 212 to the observer. The observer may examinethe organ by looking through or at the optical examination outputportion. The lens system 208 may include one or more beam splitters ordichoric mirrors 216. The lens system 208 may further include otheroptical components for directing the first light and/or the secondlight.

FIG. 3 is a schematic 300 illustrating a device according to variousembodiments. The device may be modified from the slit lamp systemillustrated in FIG. 2. The device may include the slit lamp systemillustrated in FIG. 2. The device may further include a Raman module.

Various embodiments may provide a device for eye scanning based on Ramanspectroscopy which aims to rapidly and noninvasively detect Ramanspectra from infected cornea and identify the species of microorganismscausing eye infection. The device may include a Raman module in a slitlamp ophthalmoscope so that the scanning procedure and the outlook ofthe equipment are similar to those in a routine slit lamp examination.The observer or operator may be able to conveniently switch between anoptical examination mode (also referred to as slit lamp examinationmode) and a Raman mode (using the switching mechanism) while the imagedarea (of the eye) remains unchanged. Detected Raman spectra may beprocessed by a method of multi-variate statistical analysis to identifythe species of microorganisms causing eye infection. The outcome ofRaman analysis may assist the clinician in diagnosing eye infection. Thedevice may serve as an adjunct tool to provide an alternative to thecurrent clinical procedure for the diagnosis of eye infection in theshort term. Based on the result of rapid Raman analysis, the clinicianmay decide whether the culture step is necessary to reconfirm thediagnosis and/or make appropriate treatment plans early. The wide use ofthis technique may reduce the need of the expensive and time consumingculture step in the current procedure and cut down the cost of eyeinfection management.

The device may include a first optical source 302 and a second opticalsource 304. The device may also include a detector 306. The device mayadditionally include a lens system 308. The device may further include aswitching mechanism configured to switch between an optical examinationmode and a Raman mode. The lens system 308 during optical examinationmode may be configured to direct a first light emitted from the firstoptical source 302. The lens system 308 during the Raman mode may beconfigured to direct a second light emitted from the second opticalsource 304. The lens systems 308 during the Raman mode may be furtherconfigured to direct a third light to the detector 306.

The operation of the device during the optical examination mode may besimilar to the operation of the slit lamp system illustrated in FIG. 2.The device may include an interface portion 310 and an opticalexamination output portion 312.

The interface portion 310 may be a portion of the device in which thefirst light exits from the device and in which the fourth light entersthe device. The lens system 308 may be configured to direct the firstlight emitted from the first optical source 302 to the interface portion310. The first light may be transmitted through the interface portion310 towards the eye 314. The fourth light reflected from the eye 314 maybe transmitted again through the interface portion 310. The lens system308 may be configured to direct the fourth light to an obsever such asan opthalmologist or a doctor through the optical examination outputportion 312. The lens system 308 may be further configured to direct thefourth light, the fourth light derived from the first light, from theinterface portion 310 to the optical examination output portion 312.

During the Raman examination mode, the lens system 308 during the Ramanmode may be configured to direct the second light (indicated by 318 a)emitted from the second optical source to the interface portion 310. Thesecond light may be transmitted through the interface portion towardsthe eye 314. The third light (indicated by 318 b) reflected from the eye314 may be transmitted again through the interface portion 310. thirdlight may be in an opposing direction to the second light. The device orthe lens system 308 during the Raman mode may be configured to directthe third light to the detector 306. The interface portion 310 may be aportion of the device in which the second light exits from the deviceand in which the third light enters the device.

The device may further include a function generator 320 a and a delaygenerator 320 b. The arrows 322 a, b, c may represent the flow ofcontrol signals. The function generator 320 a may be configured toprovide control signal 322 a to the second optical source 304 toactivate the second optical source 304. The function generator 320 b maybe configured to provide control signal 322 b to the delay generator 320b. The delay generator 320 b may be configured to provide control signal322 c to the detector 306 after a predetermined delay from receivingcontrol signal 322 b.

The lens system 308 may include one or more beam splitters or dichoricmirrors 316 a-c. The one or more beam splitters or dichroic mirrors 316a-c may be configured to direct the first light during the opticalexamination mode and further configured to direct the second lightduring the Raman mode. In other words, the one or more beam splitters ordichroic mirrors may be used in both modes, i.e. shared for both modes.The dichroic mirrors 316 a-c may be separate mirrors.

The lens system 308 may further include other optical components fordirecting the first light and/or the second light.

Various embodiments may provide fast and accurate Raman measurements asrequired by clinical examination. The integration of a Raman module intoa slit lamp ophthalmoscope may require careful detailed design toachieve fast and accurate Raman measurements as required by clinicalexamination which is highly challenging.

For instance, it may not practical to expect a clinician to be able tomanually focus laser light onto the area of interest on the corneasurface considering that they are not experts in optical alignment. Thefact that the cornea is transparent may make it more difficult toachieve a good focus. Unfortunately, this may be required to get a goodRaman signal.

Various embodiments may include an autofocusing method and systemdeveloped to eliminate or reduce the need of manual focusing. A redlaser adjusted at a small power following exactly the same optical pathas the excitation light for Raman excitation may be used to facilitatealignment. The laser spot for alignment, which may be visible toclinicians, may be manually moved vertically along the line illuminatedby the slit lamp to help the physician trace the location to be examinedby Raman measurements. Once the location of interest is identified, theautofocusing procedure may be started. The procedure may take advantageof the fact that the reflected light intensity increases dramaticallywhen a laser spot crosses a boundary with refractive index mismatch. Acomputer may control a piezoelectric transducer (PZT) based actuator tomove a microscope objective lens to achieve autofocusing.

The same light source for Raman excitation (working at a small power)and detector 306 (e.g. spectrograph and charged coupled device (CCD))may also be used for alignment to save the extra light source andphotodetector for alignment. The disadvantage of this approach may bethat the clinician may not be able to see the laser spot as clear as thered laser spot. Only the vertical dimension of the image in this casemay represent the spatial dimension that is used to calculate thecontrast and spot size. The other dimension (horizontal dimension) maycorrespond to the spectral dimension and should not be used for thepurpose of calculating contrast and spot size.

Another issue may be that a patient's eye may not stay at one fixedposition for long, typically only a couple of seconds or shorter. It maybe challenging to detect Raman spectra with a decent signal to noiseratio within such a short time frame.

The eye movement problem may be overcome by fast data acquisition,ideally in real time. Within the short data acquisition period, thecornea may be viewed as stationary and the effect of eye movement onRaman spectra may be neglected. To achieve this, a modified Wienerestimation for spectral reconstruction and relevant algorithms may beused to speed up data acquisition. The method of data acquisition usingthe modified Wiener estimation may be distinguished from most otherRaman systems. A spectrograph with much poorer spectral resolutioncompared to a normal one used in Raman acquisition may be used toimprove the signal to noise ratio by providing a larger bandwidth ateach wavenumber. Then a modified Wiener estimation may be used toreconstruct Raman spectra at the required spectral resolution rapidly.

The idea of using Raman spectroscopy to identify the microorganismscausing eye infection may be based on the following phenomena. First,bacteria and fungi, which are the two major microorganisms causinginfection, may exhibit unique Raman fingerprints. Second, the change inthe biochemical composition, and thus the Raman patterns, of oculartissues induced by eye infection may vary with the microorganismspecies. This may be a secondary effect compared to the Raman spectra ofthe microorganism in terms of the diagnostic value. Raman spectroscopyhas been recently used for the differential diagnosis of eye infectionand uveitis in rabbit iris in vitro and monitoring intraocular drugsagainst endophthalmitis, which demonstrates the feasibility of usingRaman spectroscopy for the in vivo identification of microorganismscausing eye infection.

However, the following requirements may have to be fulfilled in order toperform Raman measurements in the eye in vivo.

1. The excitation power density may be required to be lower than thesafety threshold prescribed in International Laser Safety Standardswhile data acquisition may need to be fast to prevent the motionartifact.

2. There may be a requirement to avoid optical alignment or realignmentwhen switching between the route slit lamp examination mode and Ramanmode.

3. Data analysis may need to be fast to provide quick feedback.

The strategies below may be employed to address these requirements oneby one.

1. A strategy may be taken to improve the signal to noise ratio in orderto achieve the goals of lowering the excitation power density and fastdata acquisition. Various filters may be used to remove the sideband inthe excitation light and the influence of ambient light on Ramanspectra. A lock-in detection scheme using a gain-modulated intensifiedCCD may be utilized to improve the signal-to-noise ratio.

2. The Raman module may be designed to minimize the changes ormodification to the slit lamp system as shown in FIG. 2A. FIG. 2B is anon-limiting example to illustrate one of several options forincorporating the Raman module. A computer code may be developed toautomate the operation of switching between the routine slit lampexamination mode and Raman mode. In other words, the switching mechanismmay include a processing circuit including a computer algorithm. Aclinician may work in the routine examination mode first to locate thetarget area. Then the scanner may be switched to Raman mode to takeRaman spectra from the area without the need of any further adjustment.

3. An ex vivo study may be carried out to identify optimal Raman bandsfor differentiating various species of microorganisms. Only selectedRaman bands may be involved in clinical data analysis to speed up thediagnosis.

By applying these strategies, a device including a Raman moduleintegrated with a slit lamp system may enable fast and sensitive Ramanmeasurements from the eye 314 without interrupting the routine slit lampexamination procedure. The high sensitivity of Raman measurements to arange of microorganisms causing infection demonstrated previously mayhelp ensure the accuracy of the non-invasive optical diagnosis. With theadvance in laser technology and sensitive optical detectors, the cost ofoptical components in a sensitive Raman system has droppedsignificantly, which may make it feasible to build a cost-effectiveRaman module for eye scanning. Due to the nature of non-contactingoptical measurements, the device may require minimum maintenance, thusfurther bringing down the total cost of operating such a system on aregular basis.

The lens system 308 may include an objective lens (not shown in FIG. 3)for focusing the second light (onto the organ such as the eye 314). Thelens system 308 may further include an actuator (not shown in FIG. 3)for controlling a position of the objective lens. By controlling theobjective lens, the actuator may control the focusing of the secondlight onto the eye 314. The actuator is a piezoelectric transducer. Theactuator may move the objective lens upon application of a voltage tothe actuator. The lens system 308 may further include an actuatorfeedback circuit (not shown in FIG. 3) coupling the detector to theactuator.

The actuator feedback circuit may be configured to receive an outputfrom the detector 306 and further configured to provide a feedback, e.g.a feedback voltage, to the actuator based on the output from thedetector 306. The actuator feedback circuit may allow for autofocusing.The actuator feedback circuit may be configured to determine a focusindex based on the output from the detector 306. The actuator feedbackcircuit may be configured to determine a plurality of focus indexesbased on a plurality of outputs the detector when the objective lens ismoved during calibration. The actuator feedback circuit may be furtherconfigured to determine a maximum focus index based on the plurality offocus indexes. The actuator may be configured to move the objective lensduring operation until the focus index is at a reference focus indexvalue, i.e. at the calibrated maximum focus index. The focusing of thefirst light onto the eye 314 may be optimal when the focus index is atthe predetermined value.

In other words, the actuator feedback circuit may be configured todetermine a focus index based on the output from the detector 306 andmay be further configured to provide a feedback, e.g. a feedbackvoltage, based on the determined focus index and a reference focusindex. The actuator feedback circuit may be further configured tocontrol the actuator to move the objective lens until the determinedfocus index is substantially equal to the reference focus index.

FIG. 4A is a schematic 400 a of a setup to illustrate focusing accordingto various embodiments. The device according to various embodiments mayinclude various components of the setup in a similar manner. In variousembodiments, references to the setup may include references to thedevice. FIG. 4B is an image 400 b of the setup illustrated in FIG. 4Aaccording to various embodiments.

The auto-focusing option may include two portions 1) hardware componentsand/or 2) software. The orientation of the experimental setup forfocusing the specific area of the sample includes both hardware andsoftware components. The hardware components used for the experimentalsetup may be illustrated in FIG. 4A. The hardware component may be usedto obtain the stack of images and the software may be used to performthe image processing.

The setup may include a laser (or LASER) source 404. The laser sourcemay also be used in the device. LASER is an acronym for lightamplification by stimulated of radiation. A laser is a component whichmay be configured to emit light by the of stimulated emission. When aparticle is hit by the photons it may absorb some energy and may jump tothe excited state from the ground state. When returning back to theoriginal position, the particle may emit some of its energy in the formof photons. Lasers may be used in many biomedical and biologicalapplications due to different

The setup may also include a beam splitter 416. The beam splitter 416may also be included in the device. As the name implies, the beamsplitter 416 may split an optical beam into two by allowing a firstlight to pass through it and a second light to be reflected atsubstantially 90° (at the point of incidence). The optical beam may betreated as the second light as highlighted earlier. The beam splitter416 may work in a similar manner to a mirror in transmitting the part ofincident light (the second light). One part of the second light may bemade to pass through the beam splitter 416 and the rest of the secondlight may be reflected from the reflecting surface of the beam splitter416.

The setup and/or device may include one or more objective lens 424 a,424 b. For the optical imaging, microscopic objective may play a majorrole in the determining the image quality and may also interpret theprimary image formation process. The primary purpose of objective lens424 b may be to collect the light from the sample or object 414 and tomagnify the information and to provide the magnified information todetector 406. Objective lens 424 b may come with different degrees ofmagnification power. The sample or object 414 may be an organ such as aneye but may also be a non-living object for the purposes of experiment.The third light reflected from the object 414 may pass through the beamsplitter 416 to the detector 406.

Objective lens 424 a, 424 b may be characterized by two parameters,namely magnification factor and numerical aperture. The objective lens424 a, 424 b may provide information by enlarging the content with aspecified range. The magnification factor for objective lens may rangefrom 40× to 100×. The numerical aperture may be defined as the acceptedangle of the lens 424 a, 424 b from which it is determined how the lens424 a, may readily emit or accept light. As numerical apertureincreases, the working distance may decrease. The working distance maybe defined as the maximum distance between sample 414 and front part ofthe lens 424 b from which all the information of the sample be collectedand it is defined as sharp focus. The parfocal length may be defined asthe distance between the objective mounting position to the samplesurface, i.e. the of focal length and working distance.

The setup or device may also include a detector such as a charge coupleddevice (CCD) 406. The detector may include a photographic film. The CCDmay include a thin silicon wafer which may be divided into a pluralityof small light sensitive areas. Each separate area or square may bereferred to as a photosite. Here each photosite may be substantiallyequivalent to a pixel of the image. Each photosite may include acapacitor which is positively charged. Basically, the CCD is an analogdevice which may convert light to electrons by photoelectric effect.Since the photosites are positively charged, the electrons, which arenegatively charged, may be attracted towards photosites. The particularmovement of charges inside the device may provide the output voltage,which may be proportional to the number of photons that are incident onthe photosites. However, the analog signal may be converted into digitalsignals. The CCD may record the video instead of taking the pictures.The cost of the scientific CCD may be expensive because the size is bigand only little silicon wafers may fit to design.

Exposure may start at the time the capacitors of the photosites arepositively charged and may end by disconnection and opening the shutterof the CCD. The light from the objective lens 424 b may be made to passthrough the silicon within the CCD. However this may lead to transfer ofsome electrons from low energy valence band to energy conduction band.Some of the electrons may be attracted towards the positively chargedcapacitor, which may allow the capacitor to discharge partially. Theamount of discharge may be directly equal or may be proportional to thenumber of photons incident on the photosite during the exposure time. Ator near the end of the exposure, the at each photosite may be amplifiedand may pass through the analog to digital converter (ADC) device todigitize the signal.

While a piezoelectric transducer (PZT) has not been used in thepreliminary experiment, a device including a piezoelectric transducermay be envisioned. A piezoelectric transducer may be a device whichconverts mechanical movement to electrical energy and electrical energyinto mechanical movement. The PZT may include a polarized material. Whenthe electric current passes through the transducer, the polarizedmaterial may realign in the different manner compared to an initialalignment, consequently producing a different shape of the material andgenerating mechanical movement. This process may be calledelectrostriction.

A fully polarized material such as quartz may generate electric energyaccording to the change in dimension of the material. This process maybe called piezoelectric effect.

The polarized material may be or may include a ceramic material, whichmay have a high efficiency to change size and shape. The efficiency ofthe transducer may be measured by the ratio of output energy to inputenergy. The efficiency of the transducer may be good when the outputenergy is greater than the input energy.

The principle of focusing technique may depend on the image spatialresolution. For the digital images, resolution may be dependent on thenumber of pixels in an image. The spatial resolution is used to findcloseness of pixels revolved to create an image. Even if the number ofpixels is high, the spatial resolution of the image may not be good. Thespatial resolution depends on the clarity of the image.

When the image is in focus, the spatial resolution may be high.Conversely, when the image is out of focus, the spatial resolution maybe lower. The determination of the focus position may be widelydependent on the spatial frequency at different planes of each imageextracted.

It may be important to align the laser beam straight because even withthe slight disturbance, it may lead to misalignment of the images atdifferent distance. The components shown in FIG. 4 are aligned in suchway that the laser beam travels all through the components in thestraight direction without any deviation.

The laser beam from the laser diode 404 may be first made to passthrough the infinite microscopic objective lens 424 a. Without using themicroscopic objective 424 a in the preliminary setup of the experiment,it was found that the images are not along the same point, i.e. theimage obtained was fluctuating along the frame with respect to differentdistances. The microscopic objective lens 424 a (M1) was included tominimize the fluctuation and to make the size of the beam larger. Thepinhole 426 a and the plano convex lens 426 b may be placed in betweenthe objective 424 a and the mirror 428 to collimate the forecoming lightin order to prevent it from diverging.

Later the beam is reflected from the mirror 428 for the sake ofrequirement of the experiment. As already mentioned, a beam splitter 416may be included to split the light into two. The beam splitter 416 mayreflect about 50% of light to the microscopic objective 424 b (M2) andallow another about 50% of light to pass through in a straight direction(not shown in FIG. 4A). The microscopic objective 424 b may play a majorrole in determining the quality of the image and to trace the focusposition. In initial experiment, objective lens 424 b is placed on themanual translator 430, which moves the objective lens 424 b forward andbackward along a direction, e.g. in the z direction, relative to thesample 414. The objective lens 424 b may be moved relative to the sample414 to achieve the best focusing.

The manual translator 430 may have a millimeter range along the mainscale and a microns range along the rotating scale. The light from theobjective lens 424 b M2 may converge at some point when moving out ofthe lens 424 b. The converging distance from the particular point to thefront of the lens 424 b may be called the working distance of theobjective lens 424 b. The point, i.e. the focus point, may be related tothe working distance of the objective lens 424 b. The objective lensused here may be purchased from Thorlabs. Here the microscopic objectiveused has the magnification factor of 40× with a working distance isabout 0.6 mm. The effective focal length of the objective lens 424 b is4.5 mm and parfocal length is about 45.06 mm. The numerical aperture ofthe objective lens 424 b is 0.65.

The reflected light (i.e. third light) from the sample 414 may passthrough the beam splitter 416. The reflected light may contain thecollected information of the sample and the detector 406 may capture theimage. Tube lens 432 a and microscopic eye piece lens 432 b may beplaced between the beam splitter 416 and CCD 406. The purpose of theobjective lens 424 b is to collect the image of the sample 414 atinfinity. The objective lens 424 b sends reflected light from the sample414 as a bundle of parallel lights across to tube lens 432 a. The tubelens 432 a behaves as a receiver and sender which may center theparallel lights from the lens 424 b to the centre part of the detector406. Microscopic eye piece lens 432 b may collect the light from tubelens 432 a so that the lights from lens 432 b are again substantiallyparallel. Tube lens 432 a may be accompanied with the microscopic eyepiece lens 432 b for obtaining better results. The main advantage ofusing the tube lens 432 a is that it provides a space between beamsplitter 416 and detector 406 so an external optical component likeanother beam splitter or filter may be included when necessary.

The working distance of the tube lens 432 a may vary according todifferent manufacturers. The tube lens may have a working distance rangefrom about 70 to about 200 mm. The focal distance of the tube lens 432 amay be about 200 mm. The tube lens 432 a may be placed at a distancefrom about 70 mm to about 200 mm from the objective lens 424 b. When thetube lens 432 a is placed at below 70 mm from the objective lens theresultant image may be affected by aberrations. Conversely, when thetube lens 432 a placed at beyond 200 mm from the objective lens 424 b,the scan lens in the tube may be overfilled, which may result ininaccurate results.

The macroscopic eye piece lens 432 b may have a different magnificationpower from tube lens 432 a. The main purpose of the macroscopic eyepiece lens 432 b is magnification. The magnification factor of 10× isused in our experiment. The light reflected from the sample 414 ismagnified accordingly and fed to the central part of the detector 406.

The detector 406 may be an Electron Multiplying Charge Coupled Device(EMCCD). The manufacture of the EMCCD used in our study is the PrincetonInstruments. ProEM cameras are designed in such a way to overcome thechallenges of low-light, high frame rate, and light-starvedapplications. A ProEM camera may include 512×512 back-illuminated EMCCDand may support both electron multiplication (EM) and traditionalreadout ports. The images of the sample 414 may be obtained by varyingthe distance of the sample 414 with the detector 406 at regularintervals.

A focusing technique based on the image spatial resolution may beprovided. As discussed earlier, the spatial resolution is a factor whichdetermines how closely the are related to form an image. When theobjective lens 424 b M2 is in focus position, the image obtained at thatposition may have a high value of the spatial resolution. When objectivelens 424 b M2 moves out of the focus position, the spatial resolution ofthe obtained may decrease. Consequently, when the image spatialresolution decreases, the high frequency components of the image mayalso be decreased. The device or setup may be configured to measure thehigh frequency components for the captured images at each plane anddetermine an optimal focus.

Generally, the high frequency content is extracted from the fullfrequency spectrum of the captured image by using a high band passfilter. The external analog filters or the digital filters inside thecomputer may be used for this purpose. In other words, the filter may bea physical filter or a digital filter.

For each turn, the manual translator 430 may be moved at constantintervals of distance. The stack of images with respect to the change indistance may be captured by the detector 406. With the help of thecomputer, digital image processing may be carried out for all the imagesto find the focus index. The focus index may be described as the ratioof the sum of square of the each pixel value of the convoluted image tothe square of the sum of the pixel values of the original image. Theconvoluted image is may be generated by convolution of the image withthe high pass filter.

The focus index may be calculated from the formula stated below,

$\begin{matrix}{{F(z)} = \frac{\sum_{x}{\sum_{y}\left\lbrack {{f\left( {x,y} \right)} \otimes {i_{2}\left( {x,y} \right)}} \right\rbrack^{2}}}{\left\lbrack {\sum_{x}{\sum_{y}{i_{z}\left( {x,y} \right)}}} \right\rbrack^{2}}} & (1)\end{matrix}$

where x, y are the index of the pixels, i_(z)(x,y) denotes the value ofeach pixels, f(x,y) is the value of high pass filter and {circle around(x)} operator denotes convolution factor

The added advantage of using the digital filters is that using thedigital filters may provide a wide number of choices to select thedifferent filters. In the experiment, the kernel integer filters areused for the image processing. The kernel filters are the most usedfilters for convoluting the image. Kernel filters may provide severaloptions like blurring, edging, sharp detection, smoothening and evenmore in the field of image processing. Generally, kernel filters mayallow both low pass and high pass filtering.

The sample or object 414 may be a glass slide with particles distributedon the glass slide. Each particle is about 10 μm in size. The sampleparticle on the glass slide may be placed in front of the objective lens424 b (M2). The manual translator 430 may be moved along a line (i.e.one dimensional translation) towards and away from the sample or object414. The objective lens 424 b may be placed as near as possible thesample or object 414 initially but without touching the glass slide. Theobjective lens is sensitive component and even a small disturbance cancause damage to lens, one should be very careful with moving theobjective lens 424 b near the sample or object 414. The objective lens424 b may be moved away from the sample or object 414 using the

.

The focus index may be calculated for each image on the detector 406according the formula. The first image may taken when the objective isplaced very near to the sample and the later images may be obtained whenmoving the objective lens away from the sample which is mounted on themanual translator 430.

FIG. 5A is a sequence 500 a of images obtained at the detector 406according to various embodiments. The images are obtained by varyingdistances of the objective lens 424 b from sample or object 414(captured with constant intervals). The images are obtained in theinterval of 0.5 microns distance. The resultant images were originallyin the tif format and it was converted to bmp file with the help ofImage J software. The images obtained was read by the computer andstored in the appropriate space for subsequent use in the program.MATLAB software was used for the image processing and to performcalculations. Any other software like C, C++ may also be used. Theadvantages of MATLAB may include that the speed is high and hence mayresult in a less time consuming process. Manual adjustment may be timeconsuming and automation may increase the speed.

The integer kernel filter may be used as the high pass filter to filterthe high frequency content of the images from the full spectrum of theimages. The integer filter

used here is,

$\quad\begin{matrix}\begin{matrix}1 & {- 2} & {- 2} \\{- 1} & {- 12} & {- 1} \\{- 1} & {- 2} & {- 1}\end{matrix} & (2)\end{matrix}$

As discussed, the first image is taken when the objective is placed verynear to the sample. The region of interest (ROI), i.e. where theparticle is spread vastly on the glass slide, is found on the firstimage. The objective lens 424 b is securely fixed to the translator 430for reducing errors. The manual translator 430 is moved away from thesample 414. The objective lens 422 b is moved away from the sample 414by moving of the manual translator.

Further images are captured at subsequent constant intervals. The totalillumination of the original image is calculated by summing up of allpixel values in each images and the whole sum is squared according toEquation (1). The convoluted image is generated by convoluting the imagewith the filter in (2).

Then the sum of square of all the gray pixels values (for the convolutedimage) is calculated. The focus index for each image is then calculatedby dividing the numerator, i.e. the square of the sum of the originalimage, with the denominator, i.e. the sum of the square of the pixelvalue of the convoluted image. The focus index values may be plotted asa function of the distance of the objective lens 424 b from the object414. FIG. 5B is a plot 500 b of the focus index as a function ofdistance in microns.

A bell shaped curve may be obtained as shown in FIG. 5B. The peak in thecurve provides the highest value of the focus index. This value mayrepresent a focus image at detector 406 when the objective lens 424 b isat an optimal distance from the object 414. The high frequency contentof the focus image may be high compared to the other images which areout of focus.

The eye is a semi-transparent object which has many layers withdifferent values of refractive index. As such, the experiment has beendone on a transparent surface. In summary, a stack of images may begenerated at the detector 406 and the focus index of each image may bedetermined. The highest value of the focus index may be determined fromthe focus indexes of the images. The image with the highest focus indexvalue may be the focus image. The spatial frequency of each image maycorrespond to the spatial resolution of the image.

FIG. 6 is a schematic 600 of a setup to illustrate auto-focusingaccording to various embodiments. The setup may be similar to the setupillustrated in FIG. 4A but with the manual translator replaced by apiezoelectric transducer 630 (such as a lead zirconium titanate (PZT)transducer) and an actuator feedback circuit 634 coupled between thedetector 406 and the piezoelectric transducer 630. The device accordingto various embodiments may include various components of the setup in asimilar manner. In various embodiments, references to the setup mayinclude references to the device.

The actuator feedback circuit 634 may be configured to receive an outputfrom the detector 406 and further configured to provide a feedback, e.g.a feedback voltage, to to the piezoelectric transducer 630 based on theoutput from the detector 406. The first calculated focus index for animage may be taken as the reference value (reference focus index). Thefeedback voltage may be provided to the piezoelectric transducer 630 foran subsequent image based on the focus index of the subsequent image.The feedback voltage may be further based on the reference focus indexor the focus index of a preceding image. The piezoelectric transducer630 may be moved accordingly towards or away from the object 414, e.g.the eye, until the focus index reaches a maximum value, i.e. when thefocus image is detected. The actuator feedback circuit 634 may beconfigured to determine a focus index based on the output from thedetector 406 and may be further configured to provide a feedback, e.g. afeedback voltage, based on the determined focus index and a referencefocus index.

FIG. 7 is a schematic 700 of a setup to illustrate line auto-focusingaccording to various embodiments. In various embodiments, references tothe setup may include references to the device.

The setup may include a laser source 704 and a detector 706 such as aspectrometer. The spectrometer may include a CCD. The setup may beconfigured to focus a light (i.e. the second light represented by 718 a)on a sample 714. The sample 714 may be an organ such as an eye but maybe a non-living object for the purposes of the experiment. The setup maybe further configured to direct a third light from the sample 714 to thedetector 706. The third light may be the second light reflected from thesample

The setup may further include a spatial light modulator (SLM) 736 formodulating the second light emitted from the laser source 704. The setupmay further include a cylindrical lens (CL) 738 so that the second lightfocused onto the sample is a curved line.

Most current autofocusing methods may be designed for focusing on asingle point. However, for Raman measurements on the eye surface, linescanning may be advantageous over point scanning because the formermethod offers much higher speed in data acquisition. There may be somedifficulties involved in line autofocusing for the purposes of eyescanning. One difficulty may be that there are currently no existingmethods for autofocusing on a line. Another difficulty may be that theeye surface is curved. The second difficulty may mean that light needsto be focused on a curve instead of a straight line. The setup mayinclude a cylindrical lens (CL) 738 and a spatial light modulator 736 toimplement line autofocusing as shown in FIG. 7. The setup may alsoinclude a filter 740, e.g. a long-pass filter (LP), between the detector706 and a dichroic filter (DF) 716.

Light (i.e. the second light) from the laser source 704 may be expandedby the beam expander (BE) 740 first. The light (i.e. the second light)may be deflected by the SLM 736 and mirror 728 (and through the dichroicfilter (DF) 716) onto the cylindrical lens (CL) 738. After passingthrough the cylindrical lens (CL) 738 and the objective lens (OBJ), thelight (i.e. the second light) may form a line focus onto the surface ofa sample to yield a bright line. The line may be a curve if the samplesurface is curved. The line may be distorted if part of the light is notfocused well. The line formed on the tissue surface may be imaged by thespectrometer 706 when the spectrograph inside the spectrometer is set tothe position acquisition mode and its central wavelength is set to zero.The image on the CCD, which is most likely blurred initially, may beskeletonized by a computer algorithm to generate a thin line. This thinline may represent the perfect image when the line focusing is achieved.The third light may be directed by the mirror 728, the SLM 736, the DF716 (and passing through LP 740) to detector 706. There may be twoalternative methods to implement autofocusing.

In the first method, the root mean square deviation between the lineimage and the skeletonized image may be used to guide the spatial lightmodulator (SLM) 736 to shape the wavefront of light, which may in turnchange the focus of each point on the line until the deviation betweenthe two images (i.e. the line image and the skeletonized image) isminimized. All points along the line focus may be consideredsimultaneously.

The device or setup may include a spatial light modulator feedbackcircuit coupling the detector 706 to the spatial light modulator (SLM)736. The spatial light modulator feedback circuit may be configured togenerate a skeletonized line (e.g. an initial skeletonized line) basedon a line (e.g. an initial line), formed by the second light the sample714 or organ). The spatial light modulator feedback circuit may beconfigured to determine a root mean square deviation between asubsequent line (i.e. the line image) and the initial skeletonized line.The spatial light modulator (SLM) 736 may be to adjust, based on afeedback from the spatial light modulator feedback circuit, until theroot mean square deviation of the subsequent line (i.e. the line image)and the initial skeletonized line is at a minimum.

In the second method, the focus index of each point in the skeletonizedimage may be calculated and fed back to the spatial light modulator(SLM) 736. The corresponding pixels in the spatial light modulator (SLM)736 may adjust their phase values to maximize the focus index. Everypoint along the line focus may be considered separately.

The device or setup may include a spatial light modulator feedbackcircuit coupling the detector 706 to the spatial light modulator (SLM)736. The spatial light modulator feedback circuit may be configured togenerate a skeletonized line, e.g. an skeletonized line based on a line,e.g. an initial line, formed by the second light (on the sample 714 ororgan). The (initial) skeletonized line may be used as a reference lineand a focus index of each pixel along the (initial) skeletonized linemay be used as reference focus index. The spatial light modulator (SLM)736 may be configured to adjust, based a feedback from the spatial lightmodulator feedback circuit, until a focus index of each pixel along asubsequent skeletonized line generated reaches a maximum value. Thefocus index of each pixel may be compared with the focus index of acorresponding pixel of the other skeletonized lines, e.g. the (initial)skeletonized line and the subsequent skeletonized lines. A pixel withthe highest value may be determined and the spatial light modulator(SLM) 736 may be configured to be adjusted (by adjusting the phase valueof each pixel of the SLM 736) so that an optimized skeletonized lineincluding a plurality of pixels, each pixel having a highest valuecompared to corresponding pixels of other skeletonized line, may begenerated on the sample 714.

Raman spectroscopy has been widely used in biomedical and clinicalapplications. Raman spectroscopy measures the inelastic scattering ofphotons induced by interaction with molecular bonds, and may thuscontain rich biochemical information. However, due to inherently weakRaman signal, Raman data acquisition may be generally slow, which mayprohibit Raman spectroscopic imaging from being used to investigate fastchanging phenomena especially in biological samples.

Several Raman imaging techniques may be developed to overcome thislimitation. Line scanned Raman imaging may collect both spatial andspectral information along a line simultaneously. Laser light may beshaped into a line using cylindrical optics or a scanning mechanism andRaman spectra may be collected by a two-dimensional detector array, i.e.charge-coupled device (CCD). While spatial information is acquired alongthe laser line, the spectral information may be collected in thedimension perpendicular to the laser line. Although the data acquisitionspeed is improved significantly, this method may cause field-curvatureartifacts. Its actual speed may be limited by the requirement ofautofocusing prior to data acquisition. Another approach for Ramanimaging may be based on acousto-optic tunable filter (AOTF) or on liquidcrystal tunable filter (LCTF). This approach may benefit from thecapability of transmitting a selectable wavelength of light usingtunable filters. However, the disadvantage of this method may includelong data acquisition when the required spectral resolution is high.Fiber array Raman imaging may also be a technique which could speed upRaman acquisition significantly. Both spatial and spectral informationmay be collected at the same time with a fiber array by rearrangingtwo-dimensional optical fibers array at the sample end toone-dimensional array on the detector end. However, the number of pixelsin the fiber array may be limited by the amount of fibers that could bemapped onto the CCD and the spatial resolution is limited by the fibersize.

Reconstruction of the Raman signal from a few narrow-band measurementsat each pixel may realize the fast Raman imaging. Only a few narrow-bandRaman images may be required and the full Raman spectrum at each pixelmay be reconstructed. Due to the small number of images required, thetraditional spectral imaging setup, i.e. using multiple filters in frontof a CCD, may work well and high spatial resolution as well as highspectral resolution may also be achieved at the same time. Dataacquisition may be much faster than most current Raman imaging setupsbased on laser scanning.

Various embodiments may include reconstructing Raman spectra in theabsence of fluorescence background. However, the drawback of this methodis that it may require a calibration data set. The calibration data setmay have to be similar to those obtained from test samples. Therefore,when a new type of samples is tested, a new calibration data set may berequired and the size of the new calibration data may be several dozensor even several hundreds. Various embodiments may seek to find a methodto suppress the calibration data size or to reduce the need of measuringthe calibration data set for every new type of samples. Fluorescencebackground may be present in a great variety of situations and itsmagnitude may be often significant compared to the Raman signal unlesssophisticated techniques, such as shifted excitation Raman differencespectroscopy, Fourier transformed Raman spectroscopy or temporal gating,are employed to suppress fluorescence.

Various embodiments may involve measuring the basic components, fromwhich most test samples are made, instead of similar samples for thecalibration purpose. Every type of samples may include several basiccomponents, and the number of basic components may be much smaller thanthe size of the traditional calibration data set. In addition, differenttypes of samples may share same basic components. If those basiccomponents have been measured before, the repeated measurements ofcalibration data may be reduced or eliminated.

The feasibility of using basic components as the calibration data sethas been demonstrated on 25 agar phantoms. The results show thepotential of using basic components instead of the traditionalcalibration data set. This method may be extended to cell spectra of anorgan such as the eye.

The phantoms were made by mixing urea (V3171, Promega corporation, US)and potassium formate (294454-500G, Sigma-Aldrich, US) in 1.5% agar(PC0701-500G, Vivantis Technologies, US) dissolved in distilled water.The concentrations of two calibration phantoms were 1 M urea and 1 Mpotassium formate respectively. The concentrations of 25 test phantomsfor both urea and potassium formate under investigation included 0.25 M,0.5 M, 1 M, 1.5 M and 2 M. The two calibration phantoms were used as thecalibration data set and the 25 test phantoms were used as the test dataset in this study.

Raman spectra were measured over a range from 600 cm⁻¹ to 1800 cm⁻¹, byusing a micro-Raman system (innoRam-7855, B&W TEK, US) coupled to avideo microscope sampling system (BAC151A, B&W TEK, US). The excitationwavelength was 785 nm and the spectral resolution was 4 cm⁻¹. Theexposure time for Raman spectra was 10 s and each spectrum wasaccumulated for 30 times.

The narrow-band measurement c was simulated according to Equation (3).

c=Fs+e  (3)

where s (m×1 matrix, in which m is the number of wavenumbers) is theRaman spectrum with fluorescence background, F (n×m matrix, in which nis the number of filters) represents the transmission spectra of thefilters and e (n×1 matrix) is the noise in narrow-narrow-bandmeasurements. In Wiener estimation, a Wiener matrix W (n×m matrix) isused to transform narrow-band measurements c (m×1 matrix) into thecorresponding Raman spectrum s̆ (n×1 matrix),

s̆=Wc  (4)

so that the mean square error between the original and estimated spectrais minimized. The Wiener matrix W is given by Equation (4).

W=K _(s) F ^(T)(FK _(s) F ^(T) +K _(e))⁻¹  (5)

where

K _(s) =E{ss ^(T) }, K _(e) =E{ee ^(T)}  (6)

In Equations (5) and (6), the superscript “T” represents matrixtranspose, the superscript “−1” represents matrix inverse and E{ }represents an ensemble average. Plugging Equation (6) into Equation (7)and ignoring the noise term yields

W=E{sc ^(T) }[E{cc ^(T)}]⁻¹  (7)

The PCs (principal components) based filter was used to generate thenarrow-band measurements. The relative root mean square error (RMSE) ofthe reconstructed Raman spectrum after the removal of fluorescencebackground, relative to the measured Raman spectrum in whichfluorescence background was also removed in the same manner, wascomputed as in Eq. (8).

$\begin{matrix}{{{Relative}\mspace{14mu} {RMSE}} = \left\lbrack \frac{\sum\limits_{i = 1}^{N}\left\lbrack {{R_{r}\left( \lambda_{i} \right)} - {R_{m}\left( \lambda_{i} \right)}} \right\rbrack^{2}}{N \times {\max \left\lbrack {R_{m}\left( \lambda_{i} \right)} \right\rbrack}^{2}} \right\rbrack^{1/2}} & (8)\end{matrix}$

where R_(r) and R_(m) are the reconstructed Raman spectrum and themeasured Raman spectrum (both after fluorescence background removed),respectively, λ_(i) is the i-th wavenumber (i is varied from 1 to N) andthe function, max[ ], returns the maximum intensity of the inputspectrum.

Table 1 shows the relative RMSE for different number of PrincipalComponents (PCs) based filters. Because there're only two basiccomponents, the number of the PCs based filters was selected up to 2.From the result, the relative RMSE improves significantly when 2 PCsbased filters were used. Table 1 shows the relative RMSE for differentnumber of PCs based filters

TABLE 1 PC number 1 2 Relative RMSE 13.9458 0.0642

FIG. 8A is a plot 800 a of intensity (arbitrary units) againstwavenumber (cm⁻¹) illustrating the best case of the test phantoms. FIG.8B is a plot 800 b of intensity (arbitrary units) against wavenumber (cm⁻¹) illustrating a typical case of the test phantoms. FIG. 8C is a plot800 c of intensity (arbitrary units) against wavenumber (cm⁻¹)illustrating the worst case of the test phantoms. 802 a represents theoriginal Raman signal while 802 b represents the reconstructed Ramansignal of the best case. 802 c represents the original Raman signalwhile 802 d represents the reconstructed Raman signal of the typicalcase. 802 e represents the original Raman signal while 802 f representsthe reconstructed Raman signal of the worst case.

The relative RMSEs for the best, typical and worst case are 0.0135,0.0703 and 0.2449, respectively. From the typical case and the worstcase, it may be noted that the peak intensities of the reconstructedRaman signal are different from the peak intensities of the originalRaman signal, but the relative intensities between the peaks within eachsample is fairly close. The reconstructed Raman spectrum and originalRaman spectrum were then normalized by dividing the intensity at eachwavenumber by the sum of the intensities at all wavenumbers. Therelative RMSE improves significantly to 0.0369 for 2 PCs based filters.As only one concentration for each basic component is used, themismatching between the reconstructed and origin intensities may be dueto the lack of intensity information for different concentrations ofbasic components. Various embodiments may include multipleconcentrations of the basic components for the calibration data set. Thesize of the calibration data set may still be small because the largeamount of combinations of the basic components is avoided and the needfor the repeated measurements of the same basic components for differenttypes of samples may be removed or reduced.

Various embodiments may provide a spectral reconstruction method basedon Wiener estimation to reconstruct Raman spectra with high spectralresolution from the narrow-band Raman measurements with fluorescencebackground.

In another experiment, a genetic algorithm is used to identify theoptimal combination of different numbers of Gaussian filters andcommercial filters for spectral reconstruction to improve accuracy. Theimportance of spectral information in the Raman signal and that influorescence background were studied by exploring two sets of components(PCs) based filters, derived separately from principal componentanalysis (PCA) of clean Raman signal and fluorescence backgroundunderlying it, for spectral reconstruction. The new strategy wasevaluated on both spontaneous Raman data and SERS data, in which theformer data represented the case of high fluorescence while the latterdata represented the case of low fluorescence background. The resultssuggest the high feasibility of eliminating the requirement ofsophisticated Raman system for fluorescence suppression in thereconstruction of Raman spectra using the Wiener estimation basedmethod. Various embodiments may provide a method applicable to a simpleand inexpensive Raman setup for fast Raman imaging that involve mostRaman spectroscopy based applications.

Spontaneous Raman data were collected from live, apoptotic and necroticleukemia cells using a micro-Raman system (inVia, Renishaw, UK) coupledto a microscope (Alpha 300, WITec, Germany) in a backscattering setup.Ten Raman spectra from each group were collected over a range from 600to 1800 cm⁻¹. The excitation wavelength was 785 nm and the spectralresolution was 2 cm⁻¹.

Surface enhanced Raman spectroscopy (SERS) data were measured from bloodserum samples collected from 50 patients with nasopharyngeal cancer inFujian Tumor hospital, Fuzhou, Fujian Province, China. Blood serumsamples were obtained by centrifugation at 2,000 rpm for 15 minutes inorder to remove blood cells and then mixed with silver colloidalnanoparticles at a size of 34 nm. The mixture was incubated for twohours at 4° C. before measurement. A confocal Raman micro-spectrometer(inVia, Renishaw, UK) was used to measure Raman spectra over a rangefrom 600 to 1800 cm⁻¹ from human blood serum. The excitation wavelengthwas 785 nm and the spectral resolution was 2 cm⁻¹. The details of samplepreparation have been described in Feng et al. (Biosensors andBioelectronics, 26, 3167, 2011) and Lin et al. Journal of RamanSpectroscopy, 43, 497, 2012).

The simulation of narrow-band measurements and methods of reconstructionand evaluation were similar to those in the previous study, which arebriefly reiterated below. Since a filter is fully characterized by itstransmission spectrum, it may be reasonable to expect that the resultshown here faithfully mimics the real situation in which Raman spectraare acquired by using these filters. The narrow-band measurement c wassimulated according to Equation (9).

c=Fs  (9)

where s (m×1 matrix, in which m is the number of wavenumbers) is theRaman spectrum with fluorescence background, F (n×m matrix, in which nis the number of filters) represents the transmission spectra of thefilters.

FIG. 9 is a schematic illustrating a procedure for Wiener estimationaccording to various embodiments. Wiener estimation may be used toreconstruct Raman spectra from simulated narrow-band measurements, whichwas performed in two stages as shown in FIG. 9. In the calibrationstage, Wiener matrix was constructed, which relates narrow-bandmeasurements to the original Raman spectra measured from samples in thecalibration set. In the test stage, Wiener matrix was applied tonarrow-band measurements from an unknown sample to reconstruct its Ramanspectrum. The Wiener matrix W may be defined in Equation (10), in whichthe noise term is ignored.

W=E(sc ^(T))[E(cc ^(T))]⁻¹  (10)

where E( ) denotes the ensemble average, the superscript “T” denotesmatrix transpose and the superscript “−1” denotes matrix inverse.

In various embodiments, the device may include a processor coupled tothe detector. The device may further include one or more filtersconfigured to generate one or more narrow-band Raman images from animage (of the organ, e.g. the eye) captured by the detector. Theprocessor may be configured to generate a reconstructed Raman imagebased on the one or more narrow Raman images.

The one or more filters may be configured to generate one or morereference narrow-band Raman images from a reference image. The processormay be configured to determine a Wiener matrix based on the one or morereference narrow-band Raman and the reference image. The processor maybe configured to generate the reconstructed Raman image based on the oneor more narrow Raman images and the Wiener matrix.

Modified Wiener estimation, which is based on traditional Wienerestimation, may improve reconstruction accuracy by synthesizing newnarrow-band measurements with an additional set of filters. In thecalibration stage, the modified Wiener matrix may be computed by thecombination of original narrow-band measurements and the synthesizednarrow-band measurements. In addition, two strategies may be used tofind the correction relations for synthesizing new narrow-bandmeasurements. In the test stage, new narrow-band measurements weresynthesized and corrected by the correction relations obtained in thecalibration stage. The modified Wiener matrix was then applied and Ramanspectra may be reconstructed accurately because the new synthesizednarrow-band measurements may provide additional information. A finalselection step may be needed to select a better one from the results ofreconstructed generated using two correction relations. More detailsabout modified Wiener estimation have been described in Chen et al(Journal of Biomedical Optics, 17, 0305011, 2012).

In order to evaluate the accuracy of a reconstructed Raman spectrum, thereconstructed Raman spectrum may be first preprocessed to removefluorescence background by using the fifth-order polynomial fitting.Then the relative root mean square error (RMSE) of the reconstructedRaman spectrum after the removal of fluorescence background, relative tothe measured Raman spectrum in which fluorescence background may alsoremoved in the same manner, was computed as in Equation (8).

Four different categories of filters were examined in this experiment,which include commercial filters, Gaussian filters, principal components(PCs) based filters and non-negative PCs based filters. These commercialfilters, Gaussian filters, PCs based filters and non-negative PCs basedfilters described herein are examples and are not intended to belimiting. Table 2 illustrates the commercial filters used in thesimulations of narrow-band measurements.

TABLE 2 Manufacturer Product numbers of commercial filters ChromaD850/20m, D850/40m Technique (Bellows Falls, VT, US) Edmund Optics NT84-790, NT 84-791 (Barrington, NJ, US) Omega Filters 3RD850LP, 3RD900LP,XB 142, XB 143, XB 146, XB (Brattleboro, 149, XF 3308, XL 19, XL 40, XLK18, XLK 20 VT, US) Semrock FF 01-830/2-25, FF 01-832/37-25, FF01-835/70-25, FF (Rochester, 01-840/12-25, FF 01-857/30-25, FF01-910/5-25 NY, US) Thorlabs FB 830-10, FB 840-10, FB 850-10, FB 850-40,FB (Newton, 860-10, FB 870-10, FB 880-10, FB 880-40, FB 890-10, NJ, US)FB 900-40, FB 910-10, FL 830-10, FL 850-10, FL 880-10, FL 905-10, FL905-25

A total of 37 commercial filters from five manufacturers wereinvestigated as shown in Table 2. The transmittance spectra of thesefilters at least partially overlap with the range of about 600 to 1about 800 cm⁻¹ an excitation wavelength of 785 nm.

A collection of 72 Gaussian filters were synthesized numerically in thisstudy. A Gaussian filter may be expressed mathematically as

$\begin{matrix}{{G(\lambda)} = {\exp\left( \frac{- \left( {\lambda - u} \right)^{2}}{2\sigma^{2}} \right)}} & (11)\end{matrix}$

where G(A) denotes the transmittance at the wavelength A, represents thecentral wavelength and a represents the standard deviation. The centralwavelength was varied over a range from 830 nm to 910 nm and theincrement was 10 nm. The standard deviation was varied over a range from2.5 nm to 20 nm and the increment was 2.5 nm.

Both PCs based filters and non-negative PCs based filters were derivedusing the principle component analysis (PCA) method. In this experiment,the transmittance spectra of PCs based filters were equivalent in shapeto the first several PCs of the Raman spectra with fluorescencebackground. The transmittance spectra of non-negative PCs based filterswere generated using the same method as in Piché (Journal OpticalSociety of America A, 19, 1946, 2002).

Genetic algorithm may be usually used to generate useful solutions foroptimization and search problems, which is based on the evolution, i.e.the survival of the fittest strategy. In the experiment, geneticalgorithm has been used to find the optimal combination of Gaussianfilters and that of commercial filters to achieve a minimal RMSE inreconstructed Raman spectra. The optimization methodology proceeded inthe following manner. Firstly, a population of filter combination wasinitialized randomly. Secondly, Wiener estimation was applied toreconstruct Raman spectra and the mean accuracy of the reconstructedRaman spectra was evaluated. Thirdly, a new population of filtercombination was generated according to the mean accuracy of thereconstructed Raman spectra, in which the filter combination yieldinghigher reconstruction accuracy is be more likely to become the parentfor the generation of the new population. The crossover rate was 0.9 andthe mutation rate was 0.1. The second and third steps were repeatediteratively until an optimized combination of filters was found. Theoptimization method was coded and run in Matlab (MATLAB R2011b,MathWorks, Natick, Mass., US).

The leave-one-out method was used for cross-validation in the experimentto fully utilize each sample in an unbiased manner. The measurement fromone sample was used as the test data each time and the measurements fromthe rest of samples were used as the calibration data set. Thisprocedure was repeated until the measurement from every sample has beentested once. For Gaussian and commercial filters, a new set of theoptimal filters and Wiener matrix were generated from the calibrationdata set by the genetic algorithm in each round of the cross-validationand then applied to the test data. For PCs based filters andnon-negative PCs based filters, the filters were fixed, thus it was notnecessary to find the optimal filters. Only Wiener matrix was generatedfrom the calibration data set in each round and then applied to the testdata.

FIG. 10A is a plot of intensity (arbitrary units) against wavenumber(cm⁻¹) illustrating an original spontaneous Raman data, includingfluorescence background, of leukemia cells. FIG. 10B is a plot ofintensity (arbitrary units) against wavenumber (cm⁻¹) illustrating anoriginal surface enhanced Raman spectroscopy (SERS) data, includingfluorescence background, of blood serum sample. Compared with SERSspectra from blood serum sample in FIG. 10B, the spontaneous Raman datafrom leukemia cells in FIG. 10A show higher variance in fluorescencebackground, which may be shown to affect the accuracy of reconstructedspectra.

Table 3 shows the cumulative contribution ratio of different PC numbersfor spontaneous Raman spectra and SERS spectra. The cumulativecontribution ratio refers to the ratio of the sum of eigenvaluescorresponding to PCs of interest to the sum of all eigenvalues. By usingup to six filters, a high percentage of 99.99% is reached in both setsof Raman spectra. Therefore, we test the filter number from three tosix, which should be sufficient for Raman reconstruction with highaccuracy.

Table 3 compares the cumulative contribution ratio of different PCnumbers for spontaneous Raman spectra and SERS spectra.

TABLE 3 Spontaneous SERS PC number Raman spectra (%) spectra (%) 2 99.6999.96 3 99.89 99.98 4 99.95 99.99 5 99.99 99.99 6 99.99 99.99 7 99.99100.00

Table 3 shows the cumulative contribution ratio of different PC numbersfor spontaneous Raman spectra and SERS spectra. The cumulativecontribution ratio refers to the ratio of the sum of eigenvaluescorresponding to PCs of interest to the sum of all eigenvalues. By usingup to six filters, a high percentage of 99.99% is reached in both setsof Raman spectra.

Three to six filters have been tested, which should be sufficient forRaman reconstruction with high accuracy. Table 4 compares the meanrelative RMSE of spontaneous Raman spectra (after fluorescencebackground removed) reconstructed from narrow-band measurements usingdifferent types and numbers of filters.

TABLE 4 Non-negative Commercial Gaussian PCs based PCs based filtersfilters filters filters 3 filters 5.61 × 10⁻² 5.18 × 10⁻² 6.93 × 10⁻²6.93 × 10⁻² 4 filters 4.91 × 10⁻² 5.07 × 10⁻² 5.83 × 10⁻² 5.83 × 10⁻² 5filters 4.01 × 10⁻² 4.37 × 10⁻² 3.20 × 10⁻² 3.20 × 10⁻² 6 filters 3.49 ×10⁻² 3.54 × 10⁻² 2.57 × 10⁻² 2.57 × 10⁻²

Table 4 shows the comparison in the mean relative RMSE of spontaneousRaman spectra (after fluorescence background removed) reconstructed fromnarrow-band measurements using different types and numbers of filters.The percentage values of reduction in the mean relative RMSE from threeto four filters were 12.5%, 2.1%, 15.9% and 15.9% for commercialfilters, Gaussian filters, PCs based filters and non-negative PCs basedfilters, respectively. The percentage values of reduction from four tofive filters were 18.3%, 13.8%, 45.1% and 45.1% and the reduction fromfive to six filters were 13.0%, 19.0%, 19.7% and 19.7%, respectively.

FIG. 11A is a plot 1100 a of intensity (arbitrary units) againstwavenumber (cm⁻¹) illustrating measured spontaneous Raman spectrum andthe spontaneous Raman spectrum reconstructed by traditional Wienerestimation for the best case using the best combination of sixcommercial filters. 1102 a indicates the measured spontaneous Ramanspectrum while 1102 b indicates the reconstructed spectrum. FIG. 11B isa plot 1100 b of intensity (arbitrary units) against wavenumber (cm⁻¹)illustrating measured spontaneous Raman spectrum and the spontaneousRaman spectrum reconstructed by traditional Wiener estimation for atypical case using the best combination of six commercial filters. 1102c indicates the measured spontaneous Raman spectrum while 1102 dindicates the reconstructed spectrum. FIG. 11C is a plot 1100 c ofintensity (arbitrary units) against wavenumber (cm⁻¹) illustratingmeasured spontaneous Raman spectrum and the spontaneous Raman spectrumreconstructed by traditional Wiener estimation for the case using thebest combination of six commercial filters. 1102 e indicates themeasured spontaneous Raman spectrum while 1102 f indicates thereconstructed spectrum. FIG. is a plot 1100 d of intensity (arbitraryunits) against wavenumber (cm⁻¹) illustrating the transmittance spectraof the six commercial filters corresponding to the typical case. 1104 arepresents the transmittance spectra of FB 860-10, 1104 b represents thetransmittance spectra of NT 84-791, 1104 c represents the transmittancespectra of FB 900-40, 1104 d represents the transmittance spectra of FF01-857, 1104 e represents the transmittance spectra of XLK 20 and 1104 frepresents the transmittance spectra of XB 143. The fluorescencebackground has been removed in both sets of spectra (measuredspontaneous Raman spectrum and the spontaneous Raman spectrumreconstructed by traditional Wiener estimation) to facilitate comparisonin Raman features.

FIGS. 11A-C show the comparison between the measured spontaneous Ramanspectra and the spontaneous Raman spectra reconstructed by traditionalWiener estimation. FIG. 11D shows the transmittance spectra of sixcommercial filters corresponding to the typical case, i.e. FB 860-10, NT84-791, FB 900-40, FF 01-857, XLK 20 and XB 143. The fluorescencebackground has been removed in both sets of spectra, i.e. the measuredspontaneous Raman spectra and the spontaneous Raman spectrareconstructed by traditional Wiener estimation, to facilitate comparisonin Raman features. The typical case (shown in FIG. 11B) is thereconstructed spontaneous Raman spectrum with a relative RMSE close tothe mean relative RMSE, while the best case (shown in FIG. 11A) andworst case (shown in FIG. 11C) are the reconstructed spontaneous Ramanspectra with the minimum relative RMSE and maximum relative RMSE. Therelative RMSEs are 1.57×10⁻², 3.29×10⁻², 6.69×10⁻² in the best case, thetypical case and the worst case, respectively.

FIG. 12A is a plot 1200 a of intensity (arbitrary units) againstwavenumber (cm⁻¹) illustrating measured spontaneous Raman spectrum andthe spontaneous Raman spectrum reconstructed by traditional Wienerestimation for the best case using the best combination of sixnon-negative principal components (PCs) based filters. 1202 a indicatesthe measured spontaneous Raman spectrum while 1202 b indicates thereconstructed spectrum. FIG. 12B is a plot 1200 b of intensity(arbitrary units) against wavenumber (cm⁻¹) illustrating measuredspontaneous Raman spectrum and the spontaneous Raman spectrumreconstructed by traditional Wiener estimation for a typical case usingthe best combination of six non-negative principal components (PCs)based filters. 1202 c indicates the measured spontaneous Raman spectrumwhile 1202 d indicates the reconstructed spectrum. FIG. 12C is a plot1200 c of intensity (arbitrary units) against wavenumber (cm⁻¹)illustrating measured spontaneous Raman spectrum and the spontaneousRaman spectrum reconstructed by traditional Wiener estimation for thecase using the best combination of six non-negative principal components(PCs) based filters. 1202 e indicates the measured spontaneous Ramanspectrum while 1202 f indicates the reconstructed spectrum. FIG. 12D isa plot 1200 d of intensity (arbitrary units) against wavenumber (cm⁻¹)illustrating the transmittance spectra of the six non-negative principalcomponents (PCs) based filters corresponding to the typical case. 1204 arepresents the transmittance spectra of the first filter, 1204 brepresents the transmittance spectra of the second filter, 1204 crepresents the transmittance spectra of the third filter, 1204 drepresents the transmittance spectra of the fourth filter, 1204 erepresents the transmittance spectra of the fifth filter and 1204 frepresents the transmittance spectra of the sixth filter. Thefluorescence background has been removed in both sets of spectra(measured spontaneous Raman spectrum and the spontaneous Raman spectrumreconstructed by traditional Wiener estimation) to facilitate comparisonin Raman features.

FIG. 12A-C show the comparison between the measured spontaneous Ramanspectra and the spontaneous Raman spectra reconstructed by traditionalWiener with the first six non-negative PCs based filters. The relativeRMSEs were 9.3×10⁻³, ×10⁻², 4.99×10⁻² in the best case, the typical caseand the worst case, respectively.

Table 5 shows the comparison in the mean relative RMSE of reconstructedSERS spectra (after fluorescence background removed) from narrow-bandmeasurements using different types and numbers of filters. Thepercentage values of reduction in the mean relative RMSE from three tofour filters were 2.7%, 2.7%, 5.1% and 5.1% for commercial filters,Gaussian filters, PCs based filters and non-negative PCs based filters,respectively. The percentage values of reduction from four to fivefilters were 1.2%, 3.9%, 19.3% and 19.3% and the percentage values ofreduction from five to six filters were 15.0%, 10.1%, 11.7% and 11.7%,respectively.

Table 5 compares the mean relative RMSE of SERS spectra (afterfluorescence background removed) reconstructed from narrow-bandmeasurements using different types and numbers of filters.

TABLE 5 Non-negative Commercial Gaussian PCs based PCs based filtersfilters filters filters 3 filters 2.65 × 10⁻² 2.64 × 10⁻² 2.13 × 10⁻²2.13 × 10⁻² 4 filters 2.57 × 10⁻² 2.57 × 10⁻² 2.02 × 10⁻² 2.02 × 10⁻² 5filters 2.54 × 10⁻² 2.47 × 10⁻² 1.63 × 10⁻² 1.63 × 10⁻² 6 filters 2.16 ×10⁻² 2.22 × 10⁻² 1.44 × 10⁻² 1.44 × 10⁻²

FIG. 13A is a plot 1300 a of intensity (arbitrary units) againstwavenumber (cm⁻¹) illustrating measured surface enhanced Ramanspectroscopy (SERS) spectrum and the surface enhanced Raman spectroscopy(SERS) Raman spectrum reconstructed by traditional Wiener estimation forthe best case using the best combination of six commercial filters. 1302a indicates the measured surface enhanced Raman spectroscopy (SERS)spectrum while 1302 b indicates the reconstructed spectrum. FIG. 13B isa plot 1300 b of intensity (arbitrary units) against wavenumber (cm⁻¹)illustrating measured surface enhanced Raman spectroscopy (SERS)spectrum and the surface enhanced spectroscopy (SERS) spectrumreconstructed by traditional Wiener estimation for a case using the bestcombination of six commercial filters. 1302 c indicates the measuredsurface enhanced Raman spectroscopy (SERS) spectrum while 1302 dindicates the reconstructed spectrum. FIG. 13C is a plot 1300 c ofintensity (arbitrary units) against wavenumber (cm⁻¹) illustratingmeasured surface enhanced Raman spectroscopy (SERS) spectrum and thesurface enhanced Raman spectroscopy (SERS) spectrum reconstructed bytraditional Wiener estimation for the worst case using the bestcombination of six commercial filters. 1302 e indicates the measuredsurface enhanced Raman spectroscopy (SERS) spectrum while 1302 findicates the reconstructed spectrum. FIG. 13D is a plot 1300 d ofintensity (arbitrary units) against wavenumber (cm⁻¹) illustrating thetransmittance spectra of the six commercial filters corresponding to thetypical case . 1304 a represents the transmittance spectra of FB 840-10,1304 b represents the transmittance spectra of FF 01-857, 1304 crepresents the transmittance spectra of FL 905-10, 1304 d represents thetransmittance spectra of FB 850-40, 1304 e represents the transmittancespectra of FF01-840 and 1304 f represents the transmittance spectra ofXB 149. The fluorescence background has been removed in both sets ofspectra (measured surface enhanced Raman spectroscopy (SERS) spectrumand the surface enhanced spectroscopy (SERS) spectrum reconstructed bytraditional Wiener estimation) to facilitate comparison in Ramanfeatures.

FIGS. 13A-C show the comparison between the measured SERS spectrum andthe SERS spectrum reconstructed by traditional Wiener estimation and thetransmittance spectra of the best combination of six commercial filterscorresponding to the typical case, i.e. FL 840-10, FF 01-857, FL 905-10,FB 850-40, FF 01-840 and XB 149. The relative RMSEs were 9.1×10⁻³,2.13×10⁻² and 5.76×10⁻² in the best case, the typical case and the worstcase, respectively.

FIG. 14A is a plot 1400 a of intensity (arbitrary units) againstwavenumber (cm⁻¹) illustrating measured surface enhanced Ramanspectroscopy (SERS) spectrum and the surface enhanced Raman spectroscopy(SERS) spectrum reconstructed by traditional Wiener estimation for thebest case using the best combination of six non-negative principalcomponents (PCs) based filters. 1402 a indicates the measured surfaceenhanced Raman spectroscopy (SERS) spectrum while 1402 b indicates thereconstructed spectrum. FIG. 14B is a plot 1400 b of intensity(arbitrary units) against wavenumber (cm⁻¹) illustrating measuredsurface enhanced Raman spectroscopy (SERS) spectrum and the surfaceenhanced Raman spectroscopy (SERS) spectrum reconstructed by traditionalWiener estimation for a typical case using the best combination of sixnon-negative principal components (PCs) based filters. 1402 c indicatesthe measured surface enhanced Raman spectroscopy (SERS) spectrum while1402 d indicates the reconstructed spectrum. FIG. 14C is a plot 1400 cof intensity (arbitrary units) against wavenumber (cm⁻¹) illustratingmeasured surface enhanced Raman spectroscopy (SERS) spectrum and thesurface enhanced Raman spectroscopy (SERS) spectrum reconstructed bytraditional Wiener estimation for the worst case using the bestcombination of six non-negative principal components (PCs) basedfilters. 1402 e indicates the measured surface enhanced Ramanspectroscopy (SERS) spectrum while 1402 f indicates the reconstructedspectrum. FIG. 14D is a plot 1400 d of intensity (arbitrary units)against wavenumber (cm⁻¹) illustrating the transmittance spectra of thesix non-negative principal components (PCs) based filters correspondingto the typical case. 1404 a represents the transmittance spectra of thefirst filter, 1404 b represents the transmittance spectra of the secondfilter, 1404 c represents the transmittance spectra of the third filter,1404 d represents the transmittance spectra of the fourth filter, 1404 erepresents the transmittance spectra of the fifth filter and 1404 frepresents the transmittance spectra of the sixth filter. Thefluorescence background has been removed in both sets of spectra(measured surface enhanced Raman spectroscopy (SERS) spectrum and thesurface enhanced Raman spectroscopy (SERS) spectrum reconstructed bytraditional Wiener estimation) to facilitate comparison in Ramanfeatures.

FIGS. 14A-C show the comparison between the measured SERS spectra andthe SERS spectra reconstructed by traditional Wiener estimation with thefirst six non-negative PCs based filters. The relative RMSEs were8.5×10⁻³, 1.44×10⁻², 2.85×10⁻² in the best case, the typical case andthe worst case, respectively.

It has been demonstrated that full Raman spectra may be reconstructed byWiener estimation from a few narrow-band measurements in the presence offluorescence background. The experiment has proved the feasibility ofapplying reconstruction based on a plurality of narrow-band measurementsby Wiener estimation, enabling fast Raman imaging using a simple Ramansetup.

For SERS spectra, Gaussian filters and commercial filters showed worseaccuracies compared with PCs based filters. This may be attributed tothe ability of PCs based filter to capture more variance, i.e.information, compared with Gaussian filters and commercial filters. Inaddition, the importance of capturing both Raman signal and fluorescencebackground information have also been verified. SERS spectra generatedfrom the Raman signal or fluorescence background alone using PCs basedfilters have been tested. For SERS spectra generated from Raman signalusing PCs based filters, the relative RMSEs were 4.57×10⁻², 4.47×10⁻²,3.28×10⁻² and 3.02×10⁻² for three, four, five and six filters,respectively. For SERS spectra generated from fluorescence backgroundusing PCs based filters, the relative RMSEs were 2.40×10⁻², 2.33×10⁻²,2.14×10⁻² and 2.03×10⁻² for three, four, five and six filters,respectively. Both sets of relative RMSEs were considerably greater thanthose obtained using PCs based filters generated from Raman spectra withfluorescence background as shown in Table 5, which implies thatinformation from both Raman signal and fluorescence background isimportant for reconstruction.

For spontaneous Raman spectra, the mean values of reconstructionaccuracy using Gaussian filters and commercial filters were better thanPCs based filters when only three or four filters were used. This may beattributed to the fluorescence background much larger in spontaneousRaman spectra compared to that in SERS spectra. In this case, thevariance for fluorescence background in a spontaneous Raman spectrum wasconsiderably larger than the Raman signal in magnitude. Based on thecharacteristics of PCA, the first three or four PCs, from which thetransmittance spectra of these PCs based filters were derived, capturemost information from smooth fluorescence background and lessinformation from the Raman signal on top of the fluorescence background.Interestingly, PCs based filters showed better reconstruction accuracythan Gaussian filters and commercial filters when five or six filterswere used. Moreover, the improvement in accuracy for PCs based filtersfrom four to five filters was significant, which means that moreinformation about Raman signal was collected by the additional PCs basedfilters as sufficient information about fluorescence background has beencollected by the first four PCs based filters. Spontaneous Raman spectrawere also generated from the Raman signal or fluorescence backgroundalone using PCs based filters. For spontaneous Raman spectra generatedfrom the Raman signal using PCs based filters, the relative RMSEs were1.43×10⁻¹, 1.43×10⁻¹, 5.60×10⁻² and 5.42×10⁻² for three, four, five andsix filters, respectively. For spontaneous Raman spectra generated fromfluorescence background using PCs based filters, the relative RMSEs were8.86×10⁻², 8.28×10⁻², 9.69×10⁻² and 8.12×10⁻² for three, four, five andsix filters, respectively. These values were much larger than thoseobtained using PCs based filters generated from Raman spectra withfluorescence background as shown in Table 4. This further shows theimportance of deriving optimal filters from both Raman signal andfluorescence background.

For both spontaneous Raman spectra and SERS spectra, additional filtersmay improve the reconstruction accuracy significantly. Therefore, atradeoff between the accuracy and cost needs to be made in the choice ofnumber of filters. Compared with spontaneous Raman spectra, thereconstruction accuracy of SERS spectra was much better when the samenumber of filters were used as shown in Tables 4 and 5. This observationcould be explained by two factors. One is that SERS spectra may containsmaller fluorescence background than spontaneous Raman spectra, whichlowers down the requirement on the effectiveness of the filter set incapturing most information. The other is that SERS spectra may exhibithigher signal-to-noise ratio, which reduces the influence of noise onreconstruction. Using sophisticated methods, e.g. shifted excitationRaman difference spectroscopy, Fourier transformed Raman spectroscopy,and temporal gating, to suppress fluorescence background and/or improvethe signal-to-noise ratio of Raman signals would further improve thereconstruction accuracy.

Table 6 compares the relative RSME of spontaneous Raman spectra (afterfluorescence background removed) reconstructed from narrow-bandmeasurements with the best combination of three filters usingtraditional Wiener estimation and between modified Wiener estimation.

TABLE 6 Non-negative Commercial Gaussian PCs based PCs based filtersfilters filters filters Traditional 5.61 × 10⁻² 5.18 × 10⁻² 6.93 × 10⁻²6.93 × 10⁻² Wiener estimation Modified 4.82 × 10⁻² 4.55 × 10⁻² 6.99 ×10⁻² 7.13 × 10⁻² Wiener estimation

Table 7 compares the relative RSME of SERS spectra (after fluorescencebackground removed) reconstructed from narrow-band measurements with thebest combination of three filters using traditional Wiener estimationand between modified Wiener estimation.

TABLE 7 Non-negative Commercial Gaussian PCs based PCs based filtersfilters filters filters Traditional 2.65 × 10⁻² 2.64 × 10⁻² 2.13 × 10⁻²2.13 × 10⁻² Wiener estimation Modified 2.54 × 10⁻² 2.61 × 10⁻² 2.14 ×10⁻² 2.15 × 10⁻² Wiener estimation

In addition, the method of modified Wiener estimation developedpreviously have been compared to traditional Wiener estimation for bothspontaneous Raman spectra and SERS spectra as shown in Tables 6 and 7.For spontaneous Raman spectra shown in Table , there were reduction inpercentage values of 14.1% and 12.2% in the relative RMSE for commercialfilters and Gaussian filters when using modified Wiener estimationcompared with the traditional Wiener estimation. In contrast, there wassmall degradation in reconstruction accuracy from traditional Wienerestimation to modified Wiener estimation for PCs based filters andnon-negative PCs based filters.

These observations may be explained as below. In modified Wienerestimation, although additional information was provided by usingsynthesized narrow-narrow-band measurements, error was also induced withthe correction process. The final reconstruction accuracy was acompromise between the gain (the additional information) and the loss(the induced error). For commercial and Gaussian filters, the additionalfilters created in the modified Wiener estimation were the first threePCs based filters. In contrast, the additional filters were the fourthto sixth PCs based filters for PCs based filters and non-negative PCsbased filters because the first three PCs filters have been applied. Thefirst three PCs based filters with relatively smooth shapes were likelyto capture additional information (mainly from slow changingfluorescence background), which outweighed the error induced (mainlyfrom sharp Raman signals) in the correction process. In comparison, thefourth to sixth PCs based filters with sharp peaks were likely to inducelarger errors in the correction process because it can only captureadditional information mainly from similarly sharp Raman peaks, whichwere more difficult to correct. For SERS spectra, which contained muchweaker fluorescence background than the spontaneous Raman spectra, shownin Table 7, there was no considerable difference in terms of therelative RMSE between modified Wiener estimation and traditional Wienerestimation. Therefore, the method of modified Wiener estimation may showmore significant advantage over traditional Wiener estimation in Ramanspectra with intense fluorescence background.

In practice, a new Wiener matrix may need to be constructed with a newset of the calibration data when using a different type of sample. Whilethis may be the major limitation for this method, this method may stillfind a large number of biomedical applications, such as differentiationof cancer from normal samples and classification of cell death mode etc.Most popular methods for such applications rely on multi-variatestatistical analysis, thus also requiring a set of data for training theclassifier.

This method of reconstruction may be advantageous when employed in Ramanimaging. Currently, most Raman imaging techniques use point scanning orline scanning, in which every scan would involve the acquisition ofRaman intensity at many wavenumbers. While wide-field Raman imagingusing a CCD may be performed at each wavenumber, this may require afilter with extremely narrow pass band and tunable central wavelength.Moreover, it may be very time consuming given the number of wavenumbersinvolved (hundreds to thousands depending on spectral resolutionrequired). Various embodiments may require only a few narrow-bandfilters with much larger bandwidths to get a few Raman images and thefull Raman spectrum at each pixel may be reconstructed. The potentialimprovement in the speed may be dramatic just considering the differencein the number of Raman images required between traditional Raman imagingand the proposed strategy.

The experiment has demonstrated a spectral reconstruction method basedon Wiener estimation applied to the narrow-band Raman measurements withfluorescence background for reconstructing the Raman spectra with highspectral resolution. The reconstruction method has been evaluated onboth spontaneous Raman data and SERS data. A genetic algorithm was usedto identify the optimal combination of different and types of filtersfor spectral construction. The agreement between reconstructed spectraand measured spectra was excellent in either set of data, whichindicates that this method may be applied to both spontaneous Ramanmeasurements and SERS measurements that involve most Raman spectroscopybased applications. The reconstruction of SERS spectra showed evenbetter results, which demonstrates that the higher signal to noise ratioand lower fluorescence background may improve the reconstructionaccuracy. For both spontaneous Raman spectra and SERS spectra, thereconstruction accuracy may be improved significantly by usingadditional filters and information from both Raman signal andfluorescence background is important. to our pervious study, the newresults suggest that the proposed method may be used in a simple Ramansystem that acquires Raman spectra with fluorescence background.Therefore, this method may open a new avenue for Raman imaging toinvestigate fast changing phenomena in biomedical applications in asimple optical setup without the function of fluorescence suppression.

FIG. 15 is a schematic 1500 illustrating a method of operating a devicefor determining a condition of an organ of either a human or an animalaccording to various embodiments. The method may include, in 1502,activating a switching mechanism to switch between an opticalexamination mode and a Raman mode. During the optical examination mode,a lens system may be configured to direct a first light emitted from afirst optical source. During the Raman mode, the lens system may beconfigured to direct a second light emitted from a second opticalsource. Further, during the Raman mode, the lens systems may be furtherconfigured to direct a third light to the detector.

In other words, the method may include switching between an opticalexamination mode and a Raman mode. The lens systems may be configured todirect a first light emitted from a first optical source during theoptical examination mode. The lens system may be configured to direct asecond light emitted from a second optical source during the Raman mode.

The first light emitted from the first optical source may incident on anorgan such as an eye. A fourth light may be reflected from the organwhen the first light is incident on the organ. The fourth light may bederived from the first light.

The second light emitted from the second optical source may incident onthe organ. The third light may be reflected from the organ when thesecond light is incident on the organ. The third light may be derivedfrom the second light. Raman analysis may be based on the second lightand third light.

A method of diagnosing an organ such as the eye may also be provided.The method may include activating a switching mechanism to switch to anoptical examination mode so that a first light may be emitted from afirst optical source, such as a non-coherent light source, to the organand detecting a disease or anomaly of the organ (by an observer such asa doctor or an optometrist) based on a second light reflected from theorgan. The method may further include activating the switching mechanismto switch to a Raman examination mode so that a second light may beemitted from a second optical source and detecting (via a detector) athird light reflected from the organ.

A method of operating a device may also be provided. The method mayactivating a switching mechanism to switch between an opticalexamination mode and a Raman mode. During the optical examination mode,a lens system may be configured to direct a first light emitted from afirst optical source. During the Raman mode, the lens system may beconfigured to direct a second light emitted from a second opticalsource. Further, during the Raman mode, the lens systems may be furtherconfigured to direct a third light to the detector.

During the optical examination mode, a lens system may be configured todirect the first light emitted from the first optical source to aninterface portion.

The lens system during the optical examination mode may be furtherconfigured to direct a fourth light, the fourth light derived from thefirst light, from the interface portion to an optical examination outputportion.

The lens system during the Raman mode may be configured to direct thesecond light emitted from the second optical source to the interfaceportion.

The lens system during the Raman mode may be configured to direct thethird light from the interface to a detector.

The third light may be derived from the second light. The fourth lightmay be derived from the first light.

In various embodiments, a use of a device may be provided. In variousembodiments, a use of a device for determining a condition of an organof either a human may be provided. The method may include activating aswitching mechanism to switch between an optical examination mode and aRaman mode. During the optical examination mode, a lens system may beconfigured to direct a first light emitted from a first optical source.During the Raman mode, the lens system may be configured to direct asecond light emitted from a second optical source. Further, during theRaman mode, the lens systems may be further configured to direct a thirdlight to the detector.

FIG. 16 is a schematic 1600 of a device for determining a condition ofan of either a human or an animal according to various embodiments. Thedevice may an optical source 1604, a detector 1606 and a lens system1608. The lens system 1608 be configured to direct a light emitted fromthe optical source 1604. The lens system may be further configured todirect a further light to the detector 1608. The further light may bebased on or derived from the light. The further light may be the lightreflected by the light incident on the organ. The lens system 1608 maybe configured to direct the to the organ. The further light reflectedfrom the organ may be directed by the lens 1608 to the detector 1606.

In various embodiments, the device illustrated in FIG. 1 may onlyinclude the components for Raman mode. The components in the deviceshown in FIG. 16 may work in a similar manner as the Raman components inthe device shown in FIG. 1.

The lens system 1608 may include an objective lens for focusing thelight. The lens system 1608 may further include an actuator, e.g. apiezoelectric transducer, for controlling a position of the objectivelens. The lens system 1608 may include an actuator feedback circuitcoupling the detector to the actuator.

The actuator feedback circuit may be configured to receive an outputfrom the detector 1606 and further configured to provide a feedback tothe actuator based on the output from the detector 1606. The actuatorfeedback circuit may be configured to determine a focus index based onthe output from the detector 1606 and further configured to provide afeedback based on the determined focus index and a reference focusindex.

The lens system may include a spatial light modulator (or anotherdynamic optical element such as a digital micromirror) for modulatingthe light emitted from the optical source reflected. The lens system mayinclude a spatial light modulator feedback circuit (or dynamic opticalelement feedback circuit) coupling the detector 1606 to the spatiallight modulator or dynamic optical element. The spatial light modulatorfeedback circuit (or dynamic optical element feedback circuit) may beconfigured to generate a skeletonized line based on a line formed by thelight. The spatial light modulator (or dynamic optical element feedbackcircuit) may be configured to be adjusted based on a feedback from thespatial light modulator feedback circuit (or dynamic optical elementfeedback circuit) until a focus index of each pixel along a subsequentskeletonized line generated reaches a maximum value. In other words, thelens system may include a dynamic optical element for modulating thelight emitted from the optical source reflected (to the organ). Thedynamic optical element may be a spatial light modulator (SLM) or adigital micromirror device. The lens system may further include adynamic optical element feedback circuit coupling the detector to thedynamic optical element. The dynamic optical element feedback circuitmay be configured to generate a skeletonized line based on a line formedby the light.

The lens system 1608 may include a single beam splitter or a singledichroic mirror configured to direct the light (and/or direct thefurther light). The device may further include a processor coupled tothe detector 1606.

The device may include one or more filters configured to generate one ornarrow-band Raman images from an image captured by the detector 1606.The processor may be configured to generate one or more reconstructedRaman images based on the one or more narrow Raman images, each of theone or more reconstructed Raman images corresponding to one wavelength.The processor may be further configured to generate a Raman spectrum ateach pixel based on the one or more reconstructed Raman images. one ormore filters may be configured to generate one or more referencenarrow-band Raman images from one or more reference images that containfull spectral information each pixel for all pixels. The processor maybe configured to determine a Wiener matrix based on the one or morereference narrow-band Raman images and the one or more reference images.The one or more reference images may be generated based on one or morereference samples, each reference sample including one or more basicbiochemical components. The processor may be configured to generate theone or more reconstructed Raman images based on the one or morenarrow-band Raman images and the Wiener matrix. The processor may beconfigured to remove fluorescence background from the or morereconstructed Raman images. The one or more narrow-band Raman images mayhave a spectral resolution lower than the one or more reconstructedRaman images. The one or more filters may be generated from one or moreprincipal components based on Raman spectra of the reference samples.

The device may have an interface portion. The lens system 1608 may beconfigured to direct the light emitted from the optical source 1604 tothe interface portion. The lens system 1608 may be configured to directthe light from the interface portion to the detector 1606. The furtherlight may have a frequency shift from the light. In other words, thefrequencies of the light and the further light may be different.

The optical source 1604 may be a laser source.

Methods described herein may further contain analogous features of anystructure, device or array described herein. Correspondingly,structures, devices or arrays described herein may further containanalogous features of any method described herein.

While the invention has been particularly shown and described withreference to specific embodiments, it should be understood by thoseskilled in the art that various changes in form and detail may be madetherein without departing from the spirit and scope of the invention asdefined by the appended claims. The scope of the invention is thusindicated by the appended claims and all changes which come within themeaning and range of equivalency of the claims are therefore intended tobe embraced.

1. A device for determining a condition of an organ of either a human oran animal, the device comprising: an optical source; a detector; and alens system; wherein the lens system is configured to direct a lightemitted from the optical source; and wherein the lens system is furtherconfigured to direct a further light to the detector.
 2. The deviceaccording to claim 1, wherein the lens system comprises an objectivelens for focusing the light emitted from the optical source.
 3. Thedevice according to claim 2, wherein the lens system further comprisesan actuator for controlling a position of the objective lens.
 4. Thedevice according to claim 3, wherein the actuator is a piezoelectrictransducer.
 5. The device according to claim 3, wherein the lens systemincludes an actuator feedback circuit coupling the detector to theactuator.
 6. The device according to claim 5, wherein the actuatorfeedback circuit is configured to receive an output from the detectorand further configured to provide a feedback to the actuator based onthe output from the detector.
 7. The device according to claim 6,wherein the actuator feedback circuit may be configured to determine afocus index based on the output from the detector and further configuredto provide a feedback based on the determined focus index and areference focus index.
 8. The device according to any of claims 1,further comprising: a dynamic optical element for modulating the lightemitted from the optical source.
 9. The device according to claim 8,wherein the lens system further comprises a dynamic optical elementfeedback circuit coupling the detector to the dynamic optical element.10. The device according to claim 9, wherein the dynamic optical elementfeedback circuit is configured to generate a skeletonized line based ona line formed by the light.
 11. The device according to claim 10,wherein the dynamic optical element is configured to be adjusted basedon a feedback from the dynamic optical element feedback circuit until afocus index of each pixel along a subsequent skeletonized line generatedreaches a maximum value.
 12. The device according to claim 8, whereinthe dynamic optical element is a spatial light modulator or a digitalmicromirror device.
 13. The device according to claim 1, wherein thelens system comprises a single beam splitter configured to direct thelight.
 14. The device according to claim 1, further comprising: aprocessor coupled to the detector.
 15. The device according to claim 14,further comprising: one or more filters configured to generate one ormore narrow-band Raman images from an image captured by the detector;wherein the processor is configured to generate one or morereconstructed Raman images based on the one or more narrow Raman images,each of the one or more reconstructed Raman images corresponding to onewavelength; and wherein the processor is further configured to generatea Raman spectrum at each pixel based on the one or more reconstructedRaman images.
 16. The device according to claim 15, wherein the one ormore filters is configured to generate one or more reference narrow-bandRaman images from one or more reference images that contain fullspectral information at each pixel for all pixels; and wherein theprocessor is configured to determine a Wiener matrix based on the one ormore reference narrow-band Raman images and the one or more referenceimages.
 17. The device according to claim 16, wherein the one or morereference images is generated based on one or more reference samples,each reference sample including one or more basic biochemicalcomponents.
 18. The device according to claim 16, wherein the processoris configured to generate the one or more reconstructed Raman imagesbased on the one or more narrow-band Raman images and the Wiener matrix.19. The device according to claim 18, wherein the processor may beconfigured to remove fluorescence background from the one or morereconstructed Raman images.
 20. The device according to 15, wherein theone or more narrow-band Raman images may have a spectral resolutionlower than the one or more reconstructed Raman images.
 21. The deviceaccording to 15, wherein the one or more filters may be generated fromone or more principal components based on Raman spectra of the referencesamples.
 22. The device according to 1, further comprising: an interfaceportion.
 23. The device according to claim 22, wherein the lens systemis configured to direct the light emitted from the optical source to theinterface portion.
 24. The device according to claim 23, wherein thelens system is configured to direct the light from the interface portionto the detector.
 25. The device according to claim 1, wherein thefurther light has a frequency shift from the light emitted from theoptical source.
 26. The device according to claim 1, wherein the opticalsource is a laser source.